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<span>Algorithm</span> </div> </a> <button aria-controls="toc-Algorithm-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> <span class="vector-icon mw-ui-icon-wikimedia-expand"></span> <span>Toggle Algorithm subsection</span> </button> <ul id="toc-Algorithm-sublist" class="vector-toc-list"> <li id="toc-AlphaFold_1_(2018)" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#AlphaFold_1_(2018)"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.1</span> <span>AlphaFold 1 (2018)</span> </div> </a> <ul id="toc-AlphaFold_1_(2018)-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-AlphaFold_2_(2020)" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#AlphaFold_2_(2020)"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.2</span> <span>AlphaFold 2 (2020)</span> </div> </a> <ul id="toc-AlphaFold_2_(2020)-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-AlphaFold_3_(2024)" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#AlphaFold_3_(2024)"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.3</span> <span>AlphaFold 3 (2024)</span> </div> </a> <ul id="toc-AlphaFold_3_(2024)-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Competitions" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Competitions"> <div class="vector-toc-text"> <span class="vector-toc-numb">3</span> <span>Competitions</span> </div> </a> <button aria-controls="toc-Competitions-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> <span class="vector-icon mw-ui-icon-wikimedia-expand"></span> <span>Toggle Competitions subsection</span> </button> <ul id="toc-Competitions-sublist" class="vector-toc-list"> <li id="toc-CASP13" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#CASP13"> <div class="vector-toc-text"> <span class="vector-toc-numb">3.1</span> <span>CASP13</span> </div> </a> <ul id="toc-CASP13-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-CASP14" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#CASP14"> <div class="vector-toc-text"> <span class="vector-toc-numb">3.2</span> <span>CASP14</span> </div> </a> <ul id="toc-CASP14-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-CASP15" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#CASP15"> <div class="vector-toc-text"> <span class="vector-toc-numb">3.3</span> <span>CASP15</span> </div> </a> <ul id="toc-CASP15-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Reception" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Reception"> <div class="vector-toc-text"> <span class="vector-toc-numb">4</span> <span>Reception</span> </div> </a> <ul id="toc-Reception-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Source_code" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Source_code"> <div class="vector-toc-text"> <span class="vector-toc-numb">5</span> <span>Source code</span> </div> </a> <ul id="toc-Source_code-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Database_of_protein_models_generated_by_AlphaFold" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Database_of_protein_models_generated_by_AlphaFold"> <div class="vector-toc-text"> <span class="vector-toc-numb">6</span> <span>Database of protein models generated by AlphaFold</span> </div> </a> <ul id="toc-Database_of_protein_models_generated_by_AlphaFold-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Limitations" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Limitations"> <div class="vector-toc-text"> <span class="vector-toc-numb">7</span> <span>Limitations</span> </div> </a> <ul id="toc-Limitations-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Applications" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Applications"> <div class="vector-toc-text"> <span class="vector-toc-numb">8</span> <span>Applications</span> </div> </a> <ul id="toc-Applications-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Published_works" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Published_works"> <div class="vector-toc-text"> <span class="vector-toc-numb">9</span> <span>Published works</span> </div> </a> <ul id="toc-Published_works-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-See_also" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#See_also"> <div class="vector-toc-text"> <span class="vector-toc-numb">10</span> <span>See also</span> </div> </a> <ul id="toc-See_also-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-References" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#References"> <div class="vector-toc-text"> <span class="vector-toc-numb">11</span> <span>References</span> </div> </a> <ul id="toc-References-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Further_reading" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Further_reading"> <div class="vector-toc-text"> <span class="vector-toc-numb">12</span> <span>Further reading</span> </div> </a> <ul id="toc-Further_reading-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-External_links" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#External_links"> <div class="vector-toc-text"> <span class="vector-toc-numb">13</span> <span>External links</span> </div> </a> <ul id="toc-External_links-sublist" class="vector-toc-list"> </ul> </li> </ul> </div> </div> </nav> </div> </div> <div class="mw-content-container"> <main id="content" class="mw-body"> <header class="mw-body-header vector-page-titlebar"> <nav aria-label="Contents" class="vector-toc-landmark"> <div id="vector-page-titlebar-toc" class="vector-dropdown vector-page-titlebar-toc vector-button-flush-left" title="Table of Contents" > <input type="checkbox" id="vector-page-titlebar-toc-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-page-titlebar-toc" class="vector-dropdown-checkbox " aria-label="Toggle the table of contents" > <label id="vector-page-titlebar-toc-label" for="vector-page-titlebar-toc-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button 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href="https://as.wikipedia.org/wiki/%E0%A6%86%E0%A6%B2%E0%A6%AB%E0%A6%BE%E0%A6%AB%E0%A6%B2%E0%A7%8D%E0%A6%A1" title="আলফাফল্ড – Assamese" lang="as" hreflang="as" data-title="আলফাফল্ড" data-language-autonym="অসমীয়া" data-language-local-name="Assamese" class="interlanguage-link-target"><span>অসমীয়া</span></a></li><li class="interlanguage-link interwiki-ca mw-list-item"><a href="https://ca.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Catalan" lang="ca" hreflang="ca" data-title="AlphaFold" data-language-autonym="Català" data-language-local-name="Catalan" class="interlanguage-link-target"><span>Català</span></a></li><li class="interlanguage-link interwiki-cs mw-list-item"><a href="https://cs.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Czech" lang="cs" hreflang="cs" data-title="AlphaFold" data-language-autonym="Čeština" data-language-local-name="Czech" class="interlanguage-link-target"><span>Čeština</span></a></li><li class="interlanguage-link interwiki-de mw-list-item"><a href="https://de.wikipedia.org/wiki/AlphaFold" title="AlphaFold – German" lang="de" hreflang="de" data-title="AlphaFold" data-language-autonym="Deutsch" data-language-local-name="German" class="interlanguage-link-target"><span>Deutsch</span></a></li><li class="interlanguage-link interwiki-el mw-list-item"><a href="https://el.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Greek" lang="el" hreflang="el" data-title="AlphaFold" data-language-autonym="Ελληνικά" data-language-local-name="Greek" class="interlanguage-link-target"><span>Ελληνικά</span></a></li><li class="interlanguage-link interwiki-es mw-list-item"><a href="https://es.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Spanish" lang="es" hreflang="es" data-title="AlphaFold" data-language-autonym="Español" data-language-local-name="Spanish" class="interlanguage-link-target"><span>Español</span></a></li><li class="interlanguage-link interwiki-fa mw-list-item"><a href="https://fa.wikipedia.org/wiki/%D8%A2%D9%84%D9%81%D8%A7%D9%81%D9%88%D9%84%D8%AF" title="آلفافولد – Persian" lang="fa" hreflang="fa" data-title="آلفافولد" data-language-autonym="فارسی" data-language-local-name="Persian" class="interlanguage-link-target"><span>فارسی</span></a></li><li class="interlanguage-link interwiki-fr mw-list-item"><a href="https://fr.wikipedia.org/wiki/AlphaFold" title="AlphaFold – French" lang="fr" hreflang="fr" data-title="AlphaFold" data-language-autonym="Français" data-language-local-name="French" class="interlanguage-link-target"><span>Français</span></a></li><li class="interlanguage-link interwiki-ga mw-list-item"><a href="https://ga.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Irish" lang="ga" hreflang="ga" data-title="AlphaFold" data-language-autonym="Gaeilge" data-language-local-name="Irish" class="interlanguage-link-target"><span>Gaeilge</span></a></li><li class="interlanguage-link interwiki-ko mw-list-item"><a href="https://ko.wikipedia.org/wiki/%EC%95%8C%ED%8C%8C%ED%8F%B4%EB%93%9C" title="알파폴드 – Korean" lang="ko" hreflang="ko" data-title="알파폴드" data-language-autonym="한국어" data-language-local-name="Korean" class="interlanguage-link-target"><span>한국어</span></a></li><li class="interlanguage-link interwiki-it mw-list-item"><a href="https://it.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Italian" lang="it" hreflang="it" data-title="AlphaFold" data-language-autonym="Italiano" data-language-local-name="Italian" class="interlanguage-link-target"><span>Italiano</span></a></li><li class="interlanguage-link interwiki-he mw-list-item"><a href="https://he.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Hebrew" lang="he" hreflang="he" data-title="AlphaFold" data-language-autonym="עברית" data-language-local-name="Hebrew" class="interlanguage-link-target"><span>עברית</span></a></li><li class="interlanguage-link interwiki-ja mw-list-item"><a href="https://ja.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Japanese" lang="ja" hreflang="ja" data-title="AlphaFold" data-language-autonym="日本語" data-language-local-name="Japanese" class="interlanguage-link-target"><span>日本語</span></a></li><li class="interlanguage-link interwiki-pt mw-list-item"><a href="https://pt.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Portuguese" lang="pt" hreflang="pt" data-title="AlphaFold" data-language-autonym="Português" data-language-local-name="Portuguese" class="interlanguage-link-target"><span>Português</span></a></li><li class="interlanguage-link interwiki-ru mw-list-item"><a href="https://ru.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Russian" lang="ru" hreflang="ru" data-title="AlphaFold" data-language-autonym="Русский" data-language-local-name="Russian" class="interlanguage-link-target"><span>Русский</span></a></li><li class="interlanguage-link interwiki-sr mw-list-item"><a href="https://sr.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Serbian" lang="sr" hreflang="sr" data-title="AlphaFold" data-language-autonym="Српски / srpski" data-language-local-name="Serbian" class="interlanguage-link-target"><span>Српски / srpski</span></a></li><li class="interlanguage-link interwiki-sv mw-list-item"><a href="https://sv.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Swedish" lang="sv" hreflang="sv" data-title="AlphaFold" data-language-autonym="Svenska" data-language-local-name="Swedish" class="interlanguage-link-target"><span>Svenska</span></a></li><li class="interlanguage-link interwiki-tr mw-list-item"><a href="https://tr.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Turkish" lang="tr" hreflang="tr" data-title="AlphaFold" data-language-autonym="Türkçe" data-language-local-name="Turkish" class="interlanguage-link-target"><span>Türkçe</span></a></li><li class="interlanguage-link interwiki-zh mw-list-item"><a href="https://zh.wikipedia.org/wiki/AlphaFold" title="AlphaFold – Chinese" lang="zh" hreflang="zh" data-title="AlphaFold" data-language-autonym="中文" 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a{color:var(--color-progressive)!important}}@media print{body.ns-0 .mw-parser-output .sidebar{display:none!important}}</style><table class="sidebar sidebar-collapse nomobile nowraplinks hlist"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="/wiki/Artificial_intelligence" title="Artificial intelligence">Artificial intelligence (AI)</a></th></tr><tr><td class="sidebar-image"><figure class="mw-halign-center" typeof="mw:File"><a href="/wiki/File:Dall-e_3_(jan_%2724)_artificial_intelligence_icon.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/6/64/Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png/100px-Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png" decoding="async" width="100" height="100" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/6/64/Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png/150px-Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/6/64/Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png/200px-Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png 2x" data-file-width="820" data-file-height="820" /></a><figcaption></figcaption></figure></td></tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/Artificial_intelligence#Goals" title="Artificial intelligence">Major goals</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a></li> <li><a href="/wiki/Intelligent_agent" title="Intelligent agent">Intelligent agent</a></li> <li><a href="/wiki/Recursive_self-improvement" title="Recursive self-improvement">Recursive self-improvement</a></li> <li><a href="/wiki/Automated_planning_and_scheduling" title="Automated planning and scheduling">Planning</a></li> <li><a href="/wiki/Computer_vision" title="Computer vision">Computer vision</a></li> <li><a href="/wiki/General_game_playing" title="General game playing">General game playing</a></li> <li><a href="/wiki/Knowledge_representation_and_reasoning" title="Knowledge representation and reasoning">Knowledge reasoning</a></li> <li><a href="/wiki/Natural_language_processing" title="Natural language processing">Natural language processing</a></li> <li><a href="/wiki/Robotics" title="Robotics">Robotics</a></li> <li><a href="/wiki/AI_safety" title="AI safety">AI safety</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)">Approaches</div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a></li> <li><a href="/wiki/Symbolic_artificial_intelligence" title="Symbolic artificial intelligence">Symbolic</a></li> <li><a href="/wiki/Deep_learning" title="Deep learning">Deep learning</a></li> <li><a href="/wiki/Bayesian_network" title="Bayesian network">Bayesian networks</a></li> <li><a href="/wiki/Evolutionary_algorithm" title="Evolutionary algorithm">Evolutionary algorithms</a></li> <li><a href="/wiki/Hybrid_intelligent_system" title="Hybrid intelligent system">Hybrid intelligent systems</a></li> <li><a href="/wiki/Artificial_intelligence_systems_integration" title="Artificial intelligence systems integration">Systems integration</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/Applications_of_artificial_intelligence" title="Applications of artificial intelligence">Applications</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Machine_learning_in_bioinformatics" title="Machine learning in bioinformatics">Bioinformatics</a></li> <li><a href="/wiki/Deepfake" title="Deepfake">Deepfake</a></li> <li><a href="/wiki/Machine_learning_in_earth_sciences" title="Machine learning in earth sciences">Earth sciences</a></li> <li><a href="/wiki/Applications_of_artificial_intelligence#Finance" title="Applications of artificial intelligence"> Finance </a></li> <li><a href="/wiki/Generative_artificial_intelligence" title="Generative artificial intelligence">Generative AI</a> <ul><li><a href="/wiki/Artificial_intelligence_art" title="Artificial intelligence art">Art</a></li> <li><a href="/wiki/Generative_audio" title="Generative audio">Audio</a></li> <li><a href="/wiki/Music_and_artificial_intelligence" title="Music and artificial intelligence">Music</a></li></ul></li> <li><a href="/wiki/Artificial_intelligence_in_government" title="Artificial intelligence in government">Government</a></li> <li><a href="/wiki/Artificial_intelligence_in_healthcare" title="Artificial intelligence in healthcare">Healthcare</a> <ul><li><a href="/wiki/Artificial_intelligence_in_mental_health" title="Artificial intelligence in mental health">Mental health</a></li></ul></li> <li><a href="/wiki/Artificial_intelligence_in_industry" title="Artificial intelligence in industry">Industry</a></li> <li><a href="/wiki/Machine_translation" title="Machine translation">Translation</a></li> <li><a href="/wiki/Artificial_intelligence_arms_race" title="Artificial intelligence arms race"> Military </a></li> <li><a href="/wiki/Machine_learning_in_physics" title="Machine learning in physics">Physics</a></li> <li><a href="/wiki/List_of_artificial_intelligence_projects" title="List of artificial intelligence projects">Projects</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/Philosophy_of_artificial_intelligence" title="Philosophy of artificial intelligence">Philosophy</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Artificial_consciousness" title="Artificial consciousness">Artificial consciousness</a></li> <li><a href="/wiki/Chinese_room" title="Chinese room">Chinese room</a></li> <li><a href="/wiki/Friendly_artificial_intelligence" title="Friendly artificial intelligence">Friendly AI</a></li> <li><a href="/wiki/AI_control_problem" class="mw-redirect" title="AI control problem">Control problem</a>/<a href="/wiki/AI_takeover" title="AI takeover">Takeover</a></li> <li><a href="/wiki/Ethics_of_artificial_intelligence" title="Ethics of artificial intelligence">Ethics</a></li> <li><a href="/wiki/Existential_risk_from_artificial_general_intelligence" class="mw-redirect" title="Existential risk from artificial general intelligence">Existential risk</a></li> <li><a href="/wiki/Turing_test" title="Turing test">Turing test</a></li> <li><a href="/wiki/Uncanny_valley" title="Uncanny valley">Uncanny valley</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/History_of_artificial_intelligence" title="History of artificial intelligence">History</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Timeline_of_artificial_intelligence" title="Timeline of artificial intelligence">Timeline</a></li> <li><a href="/wiki/Progress_in_artificial_intelligence" title="Progress in artificial intelligence">Progress</a></li> <li><a href="/wiki/AI_winter" title="AI winter">AI winter</a></li> <li><a href="/wiki/AI_boom" title="AI boom">AI boom</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)">Glossary</div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary</a></li></ul></div></div></td> </tr><tr><td class="sidebar-navbar"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374" /><style data-mw-deduplicate="TemplateStyles:r1239400231">.mw-parser-output .navbar{display:inline;font-size:88%;font-weight:normal}.mw-parser-output .navbar-collapse{float:left;text-align:left}.mw-parser-output .navbar-boxtext{word-spacing:0}.mw-parser-output .navbar ul{display:inline-block;white-space:nowrap;line-height:inherit}.mw-parser-output .navbar-brackets::before{margin-right:-0.125em;content:"[ "}.mw-parser-output .navbar-brackets::after{margin-left:-0.125em;content:" ]"}.mw-parser-output .navbar li{word-spacing:-0.125em}.mw-parser-output .navbar a>span,.mw-parser-output .navbar a>abbr{text-decoration:inherit}.mw-parser-output .navbar-mini abbr{font-variant:small-caps;border-bottom:none;text-decoration:none;cursor:inherit}.mw-parser-output .navbar-ct-full{font-size:114%;margin:0 7em}.mw-parser-output .navbar-ct-mini{font-size:114%;margin:0 4em}html.skin-theme-clientpref-night .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}}@media print{.mw-parser-output .navbar{display:none!important}}</style><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Artificial_intelligence" title="Template:Artificial intelligence"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Artificial_intelligence" title="Template talk:Artificial intelligence"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Artificial_intelligence" title="Special:EditPage/Template:Artificial intelligence"><abbr title="Edit this template">e</abbr></a></li></ul></div></td></tr></tbody></table> <p><b>AlphaFold</b> is an <a href="/wiki/Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a> (AI) program developed by <a href="/wiki/DeepMind" class="mw-redirect" title="DeepMind">DeepMind</a>, a subsidiary of <a href="/wiki/Alphabet_Inc." title="Alphabet Inc.">Alphabet</a>, which performs <a href="/wiki/Protein_structure_prediction" title="Protein structure prediction">predictions of protein structure</a>.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> It is designed using <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a> techniques.<sup id="cite_ref-mittr20201130_2-0" class="reference"><a href="#cite_note-mittr20201130-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> </p><p>AlphaFold 1 (2018) placed first in the overall rankings of the 13th <a href="/wiki/Critical_Assessment_of_Structure_Prediction" class="mw-redirect" title="Critical Assessment of Structure Prediction">Critical Assessment of Structure Prediction</a> (CASP) in December 2018. It was particularly successful at predicting the most accurate structures for targets rated as most difficult by the competition organizers, where no existing <a href="/wiki/Threading_(protein_sequence)" title="Threading (protein sequence)">template structures</a> were available from proteins with partially similar sequences. </p><p>AlphaFold 2 (2020) repeated this placement in the CASP14 competition in November 2020.<sup id="cite_ref-cnbc20201130_3-0" class="reference"><a href="#cite_note-cnbc20201130-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> It achieved a level of accuracy much higher than any other entry.<sup id="cite_ref-mittr20201130_2-1" class="reference"><a href="#cite_note-mittr20201130-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Stoddart_4-0" class="reference"><a href="#cite_note-Stoddart-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> It scored above 90 on CASP's <a href="/wiki/Global_distance_test" title="Global distance test">global distance test</a> (GDT) for approximately two-thirds of the proteins, a test measuring the similarity between a computationally predicted structure and the experimentally determined structure, where 100 represents a complete match.<sup id="cite_ref-mittr20201130_2-2" class="reference"><a href="#cite_note-mittr20201130-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-science20201130_5-0" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> The inclusion of <a href="/wiki/Metagenomics" title="Metagenomics">metagenomic</a> data has improved the quality of the prediction of <a href="/wiki/Multiple_sequence_alignment" title="Multiple sequence alignment">MSAs</a>. One of the biggest sources of the training data was the custom-built Big Fantastic Database (BFD) of 65,983,866 protein families, represented as MSAs and hidden Markov models (HMMs), covering 2,204,359,010 protein sequences from reference databases, metagenomes, and metatranscriptomes.<sup id="cite_ref-nature20210715_6-0" class="reference"><a href="#cite_note-nature20210715-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><p>AlphaFold 2's results at CASP14 were described as "astounding"<sup id="cite_ref-AlQuraishiTweet_7-0" class="reference"><a href="#cite_note-AlQuraishiTweet-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> and "transformational".<sup id="cite_ref-:5_8-0" class="reference"><a href="#cite_note-:5-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> However, some researchers noted that the accuracy was insufficient for a third of its predictions, and that it did not reveal the underlying mechanism or rules of <a href="/wiki/Protein_folding" title="Protein folding">protein folding</a> for the <a href="/wiki/Protein_folding_problem" class="mw-redirect" title="Protein folding problem">protein folding problem</a> remains unsolved.<sup id="cite_ref-curry_9-0" class="reference"><a href="#cite_note-curry-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> </p><p>Despite this, the technical achievement was widely recognized. On 15 July 2021, the AlphaFold 2 paper was published in <i><a href="/wiki/Nature_(journal)" title="Nature (journal)">Nature</a></i> as an advance access publication alongside <a href="/wiki/Open-source_software" title="Open-source software">open source software</a> and a searchable database of species <a href="/wiki/Proteome" title="Proteome">proteomes</a>.<sup id="cite_ref-nature20210715_6-1" class="reference"><a href="#cite_note-nature20210715-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup> As of February 2025, the paper had been cited nearly 32,000 times.<sup id="cite_ref-13" class="reference"><a href="#cite_note-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup> </p><p>AlphaFold 3 was announced on 8 May 2024. It can predict the structure of <a href="/wiki/Protein_complexes" class="mw-redirect" title="Protein complexes">complexes</a> created by proteins with <a href="/wiki/DNA" title="DNA">DNA</a>, <a href="/wiki/RNA" title="RNA">RNA</a>, various <a href="/wiki/Ligand_(biochemistry)" title="Ligand (biochemistry)">ligands</a>, and <a href="/wiki/Ion" title="Ion">ions</a>.<sup id="cite_ref-:0_14-0" class="reference"><a href="#cite_note-:0-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> The new prediction method shows a minimum 50% improvement in accuracy for protein interactions with other molecules compared to existing methods. Moreover, for certain key categories of interactions, the prediction accuracy has effectively doubled.<sup id="cite_ref-15" class="reference"><a href="#cite_note-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup> </p><p><a href="/wiki/Demis_Hassabis" title="Demis Hassabis">Demis Hassabis</a> and <a href="/wiki/John_M._Jumper" title="John M. Jumper">John Jumper</a> of <a href="/wiki/Google_DeepMind" title="Google DeepMind">Google DeepMind</a> shared one half of the 2024 <a href="/wiki/Nobel_Prize_in_Chemistry" title="Nobel Prize in Chemistry">Nobel Prize in Chemistry,</a> awarded "for protein structure prediction," while the other half went to <a href="/wiki/David_Baker_(biochemist)" title="David Baker (biochemist)">David Baker</a> "for computational protein design."<sup id="cite_ref-NobelChemistry2024_16-0" class="reference"><a href="#cite_note-NobelChemistry2024-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> Hassabis and Jumper had previously won the <a href="/wiki/Breakthrough_Prize_in_Life_Sciences" title="Breakthrough Prize in Life Sciences">Breakthrough Prize in Life Sciences</a> and the <a href="/wiki/Albert_Lasker_Award_for_Basic_Medical_Research" title="Albert Lasker Award for Basic Medical Research">Albert Lasker Award for Basic Medical Research</a> in 2023 for their leadership of the AlphaFold project.<sup id="cite_ref-:4_17-0" class="reference"><a href="#cite_note-:4-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:7_18-0" class="reference"><a href="#cite_note-:7-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Background">Background</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=1" title="Edit section: Background"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1236090951">.mw-parser-output .hatnote{font-style:italic}.mw-parser-output div.hatnote{padding-left:1.6em;margin-bottom:0.5em}.mw-parser-output .hatnote i{font-style:normal}.mw-parser-output .hatnote+link+.hatnote{margin-top:-0.5em}@media print{body.ns-0 .mw-parser-output .hatnote{display:none!important}}</style><div role="note" class="hatnote navigation-not-searchable">See also: <a href="/wiki/Protein_structure_prediction" title="Protein structure prediction">Protein structure prediction</a> and <a href="/wiki/De_novo_protein_structure_prediction" title="De novo protein structure prediction">De novo protein structure prediction</a></div> <figure typeof="mw:File/Thumb"><a href="/wiki/File:Protein_folding_figure.png" class="mw-file-description"><img alt="three individual polypeptide chains at different levels of folding and a cluster of chains" src="//upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Protein_folding_figure.png/300px-Protein_folding_figure.png" decoding="async" width="300" height="355" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Protein_folding_figure.png/450px-Protein_folding_figure.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Protein_folding_figure.png/600px-Protein_folding_figure.png 2x" data-file-width="1042" data-file-height="1232" /></a><figcaption>Amino-acid chains, known as <a href="/wiki/Polypeptide" class="mw-redirect" title="Polypeptide">polypeptides</a>, fold to form a protein.</figcaption></figure> <p><a href="/wiki/Protein" title="Protein">Proteins</a> consist of <a href="/wiki/Protein_primary_structure" title="Protein primary structure">chains of amino acids</a> which <a href="/wiki/Protein_folding" title="Protein folding">spontaneously fold</a> to form the <a href="/wiki/Protein_tertiary_structure" title="Protein tertiary structure">three dimensional (3-D) structures</a> of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. </p><p>Protein structures can be determined experimentally through techniques such as <a href="/wiki/X-ray_crystallography" title="X-ray crystallography">X-ray crystallography</a>, <a href="/wiki/Cryo-Electron_Microscopy" class="mw-redirect" title="Cryo-Electron Microscopy">cryo-electron microscopy</a> and <a href="/wiki/Nuclear_magnetic_resonance" title="Nuclear magnetic resonance">nuclear magnetic resonance</a>, which are all expensive and time-consuming.<sup id="cite_ref-:3_19-0" class="reference"><a href="#cite_note-:3-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup> Such efforts, using the experimental methods, have identified the structures of about 170,000 proteins over the last 60 years, while there are over 200 million known proteins across all life forms.<sup id="cite_ref-science20201130_5-1" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> </p><p>Over the years, researchers have applied numerous computational methods to <a href="/wiki/Protein_structure_prediction#Ab_initio_protein_modelling" title="Protein structure prediction">predict the 3D structures of proteins</a> from their amino acid sequences, accuracy of such methods in best possible scenario is close to experimental techniques (NMR) by the use of <a href="/wiki/Homology_modeling" title="Homology modeling">homology modeling</a> based on molecular evolution. <a href="/wiki/CASP" title="CASP">CASP</a>, which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that <a href="/wiki/Global_distance_test" title="Global distance test">GDT</a> scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016.<sup id="cite_ref-science20201130_5-2" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> AlphaFold started competing in the 2018 CASP using an <a href="/wiki/Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a> (AI) <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a> technique.<sup id="cite_ref-:3_19-1" class="reference"><a href="#cite_note-:3-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Algorithm">Algorithm</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=2" title="Edit section: Algorithm"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>DeepMind is known to have trained the program on over 170,000 proteins from the <a href="/wiki/Protein_Data_Bank" title="Protein Data Bank">Protein Data Bank</a>, a public repository of protein sequences and structures. The program uses a form of <a href="/wiki/Attention_(machine_learning)" title="Attention (machine learning)">attention network</a>, a <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a> technique that focuses on having the <a href="/wiki/AI" class="mw-redirect" title="AI">AI</a> identify parts of a larger problem, then piece it together to obtain the overall solution.<sup id="cite_ref-mittr20201130_2-3" class="reference"><a href="#cite_note-mittr20201130-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> The overall training was conducted on processing power between 100 and 200 <a href="/wiki/GPUs" class="mw-redirect" title="GPUs">GPUs</a>.<sup id="cite_ref-mittr20201130_2-4" class="reference"><a href="#cite_note-mittr20201130-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="AlphaFold_1_(2018)"><span id="AlphaFold_1_.282018.29"></span>AlphaFold 1 (2018)</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=3" title="Edit section: AlphaFold 1 (2018)"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><span class="anchor" id="AlphaFold_1"></span> </p><p><b>AlphaFold 1</b> (2018) was built on work developed by various teams in the 2010s, work that looked at the large databanks of related DNA sequences now available from many different organisms (most without known 3D structures), to try to find changes at different <a href="/wiki/Residue_(chemistry)#Biochemistry" title="Residue (chemistry)">residues</a> (peptides) that appeared to be correlated, even though the residues were not consecutive in the main chain. Such correlations suggest that the residues may be close to each other physically, even though not close in the sequence, allowing a <a href="/wiki/Contact_map" class="mw-redirect" title="Contact map">contact map</a> to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this by estimating a probability distribution for the distances between residues, effectively transforming the contact map into a distance map. It also used more advanced learning methods than previously to develop the inference.<sup id="cite_ref-20" class="reference"><a href="#cite_note-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-21" class="reference"><a href="#cite_note-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="AlphaFold_2_(2020)"><span id="AlphaFold_2_.282020.29"></span>AlphaFold 2 (2020)</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=4" title="Edit section: AlphaFold 2 (2020)"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><span class="anchor" id="AlphaFold_2"></span></p><figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:AlphaFold_2.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/2/23/AlphaFold_2.png/290px-AlphaFold_2.png" decoding="async" width="290" height="186" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/2/23/AlphaFold_2.png/435px-AlphaFold_2.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/2/23/AlphaFold_2.png/580px-AlphaFold_2.png 2x" data-file-width="2157" data-file-height="1382" /></a><figcaption>AlphaFold 2 performance, experiments, and architecture<sup id="cite_ref-nature_22-0" class="reference"><a href="#cite_note-nature-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup></figcaption></figure> <figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:Architectural_details_of_AlphaFold_2.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/3/31/Architectural_details_of_AlphaFold_2.png/290px-Architectural_details_of_AlphaFold_2.png" decoding="async" width="290" height="217" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/3/31/Architectural_details_of_AlphaFold_2.png/435px-Architectural_details_of_AlphaFold_2.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/3/31/Architectural_details_of_AlphaFold_2.png/580px-Architectural_details_of_AlphaFold_2.png 2x" data-file-width="2149" data-file-height="1610" /></a><figcaption>Architectural details of AlphaFold 2<sup id="cite_ref-nature_22-1" class="reference"><a href="#cite_note-nature-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup></figcaption></figure> <p>The 2020 version of the program (<b>AlphaFold 2</b>, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind.<sup id="cite_ref-economist20201130_23-0" class="reference"><a href="#cite_note-economist20201130-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-KahnLessons_24-0" class="reference"><a href="#cite_note-KahnLessons-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup> </p><p>AlphaFold 1 used a number of separately trained modules to produce a guide potential, which was then combined with a physics-based energy potential. AlphaFold 2 replaced this with a system of interconnected sub-networks, forming a single, differentiable, end-to-end model based on pattern recognition. This model was trained in an integrated manner.<sup id="cite_ref-KahnLessons_24-1" class="reference"><a href="#cite_note-KahnLessons-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-block_diagram_25-0" class="reference"><a href="#cite_note-block_diagram-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> After the neural network's prediction converges, a final refinement step applies local physical constraints using energy minimization based on the <a href="/wiki/AMBER" title="AMBER">AMBER</a> force field. This step only slightly adjusts the predicted structure.<sup id="cite_ref-Alpha2Abstract_26-0" class="reference"><a href="#cite_note-Alpha2Abstract-26"><span class="cite-bracket">[</span>26<span class="cite-bracket">]</span></a></sup> </p><p>A key part of the 2020 system are two modules, believed to be based on a <a href="/wiki/Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">transformer</a> design, which are used to progressively refine a <a href="/wiki/Word_embedding" title="Word embedding">vector of information</a> for each relationship (or "<a href="/wiki/Connectivity_(graph_theory)" title="Connectivity (graph theory)">edge</a>" in graph-theory terminology) between an <a href="/wiki/Amino_acid_residue" class="mw-redirect" title="Amino acid residue">amino acid residue</a> of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input <a href="/wiki/Sequence_alignment" title="Sequence alignment">sequence alignment</a> (these relationships are represented by the array shown in red).<sup id="cite_ref-block_diagram_25-1" class="reference"><a href="#cite_note-block_diagram-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learnt from training data. These transformations are iterated, the updated information output by one step becoming the input of the next, with the sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information.<sup id="cite_ref-block_diagram_25-2" class="reference"><a href="#cite_note-block_diagram-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> As the iteration progresses, according to one report, the "attention algorithm ... mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole."<sup id="cite_ref-science20201130_5-3" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup><sup class="noprint Inline-Template" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Manual_of_Style/Dates_and_numbers#Chronological_items" title="Wikipedia:Manual of Style/Dates and numbers"><span title="More information was made available in DeepMind's paper in July 2021 (see in particular the "Evoformer" part) (May 2024)">needs update</span></a></i>]</sup> </p><p>The output of these iterations then informs the final structure prediction module,<sup id="cite_ref-block_diagram_25-3" class="reference"><a href="#cite_note-block_diagram-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> which also uses transformers,<sup id="cite_ref-27" class="reference"><a href="#cite_note-27"><span class="cite-bracket">[</span>27<span class="cite-bracket">]</span></a></sup> and is itself then iterated. In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations – i.e. unphysical bond angles or lengths. With subsequent iterations the number of stereochemical violations fell. By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero.<sup id="cite_ref-AF2iterations_28-0" class="reference"><a href="#cite_note-AF2iterations-28"><span class="cite-bracket">[</span>28<span class="cite-bracket">]</span></a></sup> </p><p>The training data was originally restricted to single peptide chains. However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data. DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions.<sup id="cite_ref-29" class="reference"><a href="#cite_note-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="AlphaFold_3_(2024)"><span id="AlphaFold_3_.282024.29"></span>AlphaFold 3 (2024)</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=5" title="Edit section: AlphaFold 3 (2024)"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><span class="anchor" id="AlphaFold_3"></span> </p><p>Announced on 8 May 2024, <b>AlphaFold 3</b> was co-developed by Google DeepMind and <a href="/wiki/Isomorphic_Labs" title="Isomorphic Labs">Isomorphic Labs</a>, both subsidiaries of <a href="/wiki/Alphabet_Inc." title="Alphabet Inc.">Alphabet</a>. AlphaFold 3 is not limited to single-chain proteins, as it can also predict the structures of <a href="/wiki/Protein_complex" title="Protein complex">protein complexes</a> with <a href="/wiki/DNA" title="DNA">DNA</a>, <a href="/wiki/RNA" title="RNA">RNA</a>, <a href="/wiki/Post-translational_modification" title="Post-translational modification">post-translational modifications</a> and selected <a href="/wiki/Ligand" title="Ligand">ligands</a> and <a href="/wiki/Ion" title="Ion">ions</a>.<sup id="cite_ref-30" class="reference"><a href="#cite_note-30"><span class="cite-bracket">[</span>30<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:0_14-1" class="reference"><a href="#cite_note-:0-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> </p><p>AlphaFold 3 introduces the "Pairformer," a deep learning architecture inspired by the transformer, which is considered similar to, but simpler than, the Evoformer used in AlphaFold 2.<sup id="cite_ref-31" class="reference"><a href="#cite_note-31"><span class="cite-bracket">[</span>31<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-32" class="reference"><a href="#cite_note-32"><span class="cite-bracket">[</span>32<span class="cite-bracket">]</span></a></sup> The Pairformer module's initial predictions are refined by a <a href="/wiki/Diffusion_model" title="Diffusion model">diffusion model</a>. This model begins with a cloud of atoms and iteratively refines their positions, guided by the Pairformer's output, to generate a 3D representation of the molecular structure.<sup id="cite_ref-:0_14-2" class="reference"><a href="#cite_note-:0-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> </p><p>The AlphaFold server was created to provide free access to AlphaFold 3 for non-commercial research.<sup id="cite_ref-33" class="reference"><a href="#cite_note-33"><span class="cite-bracket">[</span>33<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Competitions">Competitions</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=6" title="Edit section: Competitions"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <figure class="mw-halign-right" typeof="mw:File/Thumb"><a href="/wiki/File:CASP_results_2020.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/en/thumb/7/79/CASP_results_2020.png/500px-CASP_results_2020.png" decoding="async" width="500" height="281" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/7/79/CASP_results_2020.png/750px-CASP_results_2020.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/7/79/CASP_results_2020.png/1000px-CASP_results_2020.png 2x" data-file-width="1333" data-file-height="750" /></a><figcaption>Results achieved for protein prediction by the best reconstructions in the CASP 2018 competition (small circles) and CASP 2020 competition (large circles), compared with results achieved in previous years.<br />The crimson trend-line shows how a handful of models including AlphaFold 1 achieved a significant step-change in 2018 over the rate of progress that had previously been achieved, particularly in respect of the protein sequences considered the most difficult to predict.<br /> (Qualitative improvement had been made in earlier years, but it is only as changes bring structures within 8 <a href="/wiki/Angstrom" title="Angstrom">Å</a> of their experimental positions that they start to affect the CASP GDS-TS measure).<br /> The orange trend-line shows that by 2020 online prediction servers had been able to learn from and match this performance, while the best other groups (green curve) had on average been able to make some improvements on it. However, the black trend curve shows the degree to which AlphaFold 2 had surpassed this again in 2020, across the board.<br /> The detailed spread of data points indicates the degree of consistency or variation achieved by AlphaFold. Outliers represent the handful of sequences for which it did not make such a successful prediction.</figcaption></figure> <div class="mw-heading mw-heading3"><h3 id="CASP13">CASP13</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=7" title="Edit section: CASP13"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In December 2018, DeepMind's AlphaFold placed first in the overall rankings of the 13th <a href="/wiki/Critical_Assessment_of_Techniques_for_Protein_Structure_Prediction" class="mw-redirect" title="Critical Assessment of Techniques for Protein Structure Prediction">Critical Assessment of Techniques for Protein Structure Prediction</a> (CASP).<sup id="cite_ref-34" class="reference"><a href="#cite_note-34"><span class="cite-bracket">[</span>34<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Guardian2018_35-0" class="reference"><a href="#cite_note-Guardian2018-35"><span class="cite-bracket">[</span>35<span class="cite-bracket">]</span></a></sup> </p><p>The program was particularly successfully predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing <a href="/wiki/Threading_(protein_sequence)" title="Threading (protein sequence)">template structures</a> were available from proteins with a partially similar sequence. AlphaFold gave the best prediction for 25 out of 43 protein targets in this class,<sup id="cite_ref-Guardian2018_35-1" class="reference"><a href="#cite_note-Guardian2018-35"><span class="cite-bracket">[</span>35<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-36" class="reference"><a href="#cite_note-36"><span class="cite-bracket">[</span>36<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-37" class="reference"><a href="#cite_note-37"><span class="cite-bracket">[</span>37<span class="cite-bracket">]</span></a></sup> achieving a median score of 58.9 on the CASP's <a href="/wiki/Global_distance_test" title="Global distance test">global distance test</a> (GDT) score, ahead of 52.5 and 52.4 by the two next best-placed teams,<sup id="cite_ref-38" class="reference"><a href="#cite_note-38"><span class="cite-bracket">[</span>38<span class="cite-bracket">]</span></a></sup> who were also using deep learning to estimate contact distances.<sup id="cite_ref-39" class="reference"><a href="#cite_note-39"><span class="cite-bracket">[</span>39<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-40" class="reference"><a href="#cite_note-40"><span class="cite-bracket">[</span>40<span class="cite-bracket">]</span></a></sup> Overall, across all targets, AlphaFold 1 achieved a GDT score of 68.5.<sup id="cite_ref-:2_41-0" class="reference"><a href="#cite_note-:2-41"><span class="cite-bracket">[</span>41<span class="cite-bracket">]</span></a></sup> </p><p>In January 2020, implementations and illustrative code of AlphaFold 1 was released <a href="/wiki/Open-source_software" title="Open-source software">open-source</a> on <a href="/wiki/GitHub" title="GitHub">GitHub</a>.<sup id="cite_ref-42" class="reference"><a href="#cite_note-42"><span class="cite-bracket">[</span>42<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:3_19-2" class="reference"><a href="#cite_note-:3-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup> but, as stated in the "Read Me" file on that website: "This code can't be used to predict structure of an arbitrary protein sequence. It can be used to predict structure only on the CASP13 dataset (links below). The feature generation code is tightly coupled to our internal infrastructure as well as external tools, hence we are unable to open-source it." Therefore, in essence, the code deposited is not suitable for general use but only for the CASP13 proteins. The company has not announced plans to make their code publicly available as of 5 March 2021. </p> <div class="mw-heading mw-heading3"><h3 id="CASP14">CASP14</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=8" title="Edit section: CASP14"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In November 2020, DeepMind's new version, AlphaFold 2, won CASP14.<sup id="cite_ref-DeepMindAlpha2_43-0" class="reference"><a href="#cite_note-DeepMindAlpha2-43"><span class="cite-bracket">[</span>43<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-44" class="reference"><a href="#cite_note-44"><span class="cite-bracket">[</span>44<span class="cite-bracket">]</span></a></sup> Overall, AlphaFold 2 made the best prediction for 88 out of the 97 targets.<sup id="cite_ref-AlQuraishiTweet_7-1" class="reference"><a href="#cite_note-AlQuraishiTweet-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> </p><p>On the competition's preferred <a href="/wiki/Global_distance_test" title="Global distance test">global distance test</a> (GDT) measure of accuracy, the program achieved a median score of 92.4 (out of 100), meaning that more than half of its predictions were scored at better than 92.4% for having their atoms in more-or-less the right place,<sup id="cite_ref-45" class="reference"><a href="#cite_note-45"><span class="cite-bracket">[</span>45<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-46" class="reference"><a href="#cite_note-46"><span class="cite-bracket">[</span>46<span class="cite-bracket">]</span></a></sup> a level of accuracy reported to be comparable to experimental techniques like <a href="/wiki/X-ray_crystallography" title="X-ray crystallography">X-ray crystallography</a>.<sup id="cite_ref-economist20201130_23-1" class="reference"><a href="#cite_note-economist20201130-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:5_8-1" class="reference"><a href="#cite_note-:5-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:2_41-1" class="reference"><a href="#cite_note-:2-41"><span class="cite-bracket">[</span>41<span class="cite-bracket">]</span></a></sup> In 2018 AlphaFold 1 had only reached this level of accuracy in two of all of its predictions.<sup id="cite_ref-AlQuraishiTweet_7-2" class="reference"><a href="#cite_note-AlQuraishiTweet-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> 88% of predictions in the 2020 competition had a GDT_TS score of more than 80. On the group of targets classed as the most difficult, AlphaFold 2 achieved a median score of 87. </p><p>Measured by the <a href="/wiki/Root-mean-square_deviation_of_atomic_positions" class="mw-redirect" title="Root-mean-square deviation of atomic positions">root-mean-square deviation</a> (RMS-D) of the placement of the alpha-carbon atoms of the protein backbone chain, which tends to be dominated by the performance of the worst-fitted outliers, 88% of AlphaFold 2's predictions had an RMS deviation of less than 4 <a href="/wiki/Angstrom" title="Angstrom">Å</a> for the set of overlapped C-alpha atoms.<sup id="cite_ref-AlQuraishiTweet_7-3" class="reference"><a href="#cite_note-AlQuraishiTweet-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> 76% of predictions achieved better than 3 Å, and 46% had a C-alpha atom RMS accuracy better than 2 Å,<sup id="cite_ref-AlQuraishiTweet_7-4" class="reference"><a href="#cite_note-AlQuraishiTweet-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> with a median RMS deviation in its predictions of 2.1 Å for a set of overlapped CA atoms.<sup id="cite_ref-AlQuraishiTweet_7-5" class="reference"><a href="#cite_note-AlQuraishiTweet-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> AlphaFold 2 also achieved an accuracy in modelling surface <a href="/wiki/Side_chain" title="Side chain">side chains</a> described as "really really extraordinary". </p><p>To further validate AlphaFold 2, the conference organizers approached four leading experimental groups working on structures they found particularly challenging and had been unable to determine. In all four cases the three-dimensional models produced by AlphaFold 2 were sufficiently accurate to determine structures of these proteins by <a href="/wiki/Molecular_replacement" title="Molecular replacement">molecular replacement</a>. These included target T1100 (Af1503), a small <a href="/wiki/Membrane_protein" title="Membrane protein">membrane protein</a> studied by experimentalists for ten years.<sup id="cite_ref-science20201130_5-4" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> </p><p>Of the three structures that AlphaFold 2 had the least success in predicting, two had been obtained by <a href="/wiki/Nuclear_magnetic_resonance_spectroscopy_of_proteins" title="Nuclear magnetic resonance spectroscopy of proteins">protein NMR</a> methods, which define protein structure directly in aqueous solution, whereas AlphaFold was mostly trained on <a href="/wiki/X-ray_crystallography" title="X-ray crystallography">protein structures in crystals</a>. The third exists in nature as a <a href="/wiki/Protein_domain#Multidomain_proteins" title="Protein domain">multidomain complex</a> consisting of 52 identical copies of the same <a href="/wiki/Protein_domain" title="Protein domain">domain</a>, a situation AlphaFold was not programmed to consider. For all targets with a single domain, excluding only one very large protein and the two structures determined by NMR, AlphaFold 2 achieved a GDT_TS score of over 80. </p> <div class="mw-heading mw-heading3"><h3 id="CASP15">CASP15</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=9" title="Edit section: CASP15"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In 2022, DeepMind did not enter CASP15, but most of the entrants used AlphaFold or tools incorporating AlphaFold.<sup id="cite_ref-47" class="reference"><a href="#cite_note-47"><span class="cite-bracket">[</span>47<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Reception">Reception</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=10" title="Edit section: Reception"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>AlphaFold 2 scoring more than 90 in <a href="/wiki/CASP" title="CASP">CASP</a>'s <a href="/wiki/Global_distance_test" title="Global distance test">global distance test</a> (GDT) is considered a significant achievement in <a href="/wiki/Computational_biology" title="Computational biology">computational biology</a><sup id="cite_ref-science20201130_5-5" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> and great progress towards a decades-old grand challenge of biology.<sup id="cite_ref-:5_8-2" class="reference"><a href="#cite_note-:5-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Nobel_Prize_in_Chemistry" title="Nobel Prize in Chemistry">Nobel Prize</a> winner and <a href="/wiki/Structural_biology" title="Structural biology">structural biologist</a> <a href="/wiki/Venki_Ramakrishnan" title="Venki Ramakrishnan">Venki Ramakrishnan</a> called the result "a stunning advance on the protein folding problem",<sup id="cite_ref-science20201130_5-6" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> adding that "It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research."<sup id="cite_ref-DeepMindAlpha2_43-1" class="reference"><a href="#cite_note-DeepMindAlpha2-43"><span class="cite-bracket">[</span>43<span class="cite-bracket">]</span></a></sup> </p><p>Propelled by press releases from CASP and DeepMind,<sup id="cite_ref-CASP_release_48-0" class="reference"><a href="#cite_note-CASP_release-48"><span class="cite-bracket">[</span>48<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-DeepMindAlpha2_43-2" class="reference"><a href="#cite_note-DeepMindAlpha2-43"><span class="cite-bracket">[</span>43<span class="cite-bracket">]</span></a></sup> AlphaFold 2's success received wide media attention.<sup id="cite_ref-49" class="reference"><a href="#cite_note-49"><span class="cite-bracket">[</span>49<span class="cite-bracket">]</span></a></sup> As well as news pieces in the specialist science press, such as <i><a href="/wiki/Nature_(journal)" title="Nature (journal)">Nature</a></i>,<sup id="cite_ref-:5_8-3" class="reference"><a href="#cite_note-:5-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> <i><a href="/wiki/Science_(journal)" title="Science (journal)">Science</a></i>,<sup id="cite_ref-science20201130_5-7" class="reference"><a href="#cite_note-science20201130-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> <i><a href="/wiki/MIT_Technology_Review" title="MIT Technology Review">MIT Technology Review</a></i>,<sup id="cite_ref-mittr20201130_2-5" class="reference"><a href="#cite_note-mittr20201130-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> and <i><a href="/wiki/New_Scientist" title="New Scientist">New Scientist</a></i>,<sup id="cite_ref-50" class="reference"><a href="#cite_note-50"><span class="cite-bracket">[</span>50<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-51" class="reference"><a href="#cite_note-51"><span class="cite-bracket">[</span>51<span class="cite-bracket">]</span></a></sup> the story was widely covered by major national newspapers,.<sup id="cite_ref-52" class="reference"><a href="#cite_note-52"><span class="cite-bracket">[</span>52<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-53" class="reference"><a href="#cite_note-53"><span class="cite-bracket">[</span>53<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-54" class="reference"><a href="#cite_note-54"><span class="cite-bracket">[</span>54<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-55" class="reference"><a href="#cite_note-55"><span class="cite-bracket">[</span>55<span class="cite-bracket">]</span></a></sup> A frequent theme was that ability to predict protein structures accurately based on the constituent amino acid sequence is expected to have a wide variety of benefits in the life sciences space including accelerating advanced drug discovery and enabling better understanding of diseases.<sup id="cite_ref-:5_8-4" class="reference"><a href="#cite_note-:5-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-56" class="reference"><a href="#cite_note-56"><span class="cite-bracket">[</span>56<span class="cite-bracket">]</span></a></sup> Some have noted that even a perfect answer to the protein <i><a href="/wiki/Protein_structure_prediction" title="Protein structure prediction">prediction</a></i> problem would still leave questions about the protein <i><a href="/wiki/Protein_folding" title="Protein folding">folding</a></i> problem—understanding in detail how the folding process actually occurs in nature (and how sometimes they can also <a href="/wiki/Proteopathy" class="mw-redirect" title="Proteopathy">misfold</a>).<sup id="cite_ref-57" class="reference"><a href="#cite_note-57"><span class="cite-bracket">[</span>57<span class="cite-bracket">]</span></a></sup> </p><p>In 2023, <a href="/wiki/Demis_Hassabis" title="Demis Hassabis">Demis Hassabis</a> and <a href="/wiki/John_M._Jumper" title="John M. Jumper">John Jumper</a> won the <a href="/wiki/Breakthrough_Prize_in_Life_Sciences" title="Breakthrough Prize in Life Sciences">Breakthrough Prize in Life Sciences</a><sup id="cite_ref-:7_18-1" class="reference"><a href="#cite_note-:7-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> as well as the <a href="/wiki/Albert_Lasker_Award_for_Basic_Medical_Research" title="Albert Lasker Award for Basic Medical Research">Albert Lasker Award for Basic Medical Research</a> for their management of the AlphaFold project.<sup id="cite_ref-58" class="reference"><a href="#cite_note-58"><span class="cite-bracket">[</span>58<span class="cite-bracket">]</span></a></sup> Hassabis and Jumper proceeded to win the <a href="/wiki/Nobel_Prize_in_Chemistry" title="Nobel Prize in Chemistry">Nobel Prize in Chemistry</a> in 2024 for their work on “protein structure prediction” with <a href="/wiki/David_Baker_(biochemist)" title="David Baker (biochemist)">David Baker</a> of the University of Washington.<sup id="cite_ref-:4_17-1" class="reference"><a href="#cite_note-:4-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-59" class="reference"><a href="#cite_note-59"><span class="cite-bracket">[</span>59<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Source_code">Source code</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=11" title="Edit section: Source code"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Open access to source code of several AlphaFold versions (excluding AlphaFold 3) has been provided by DeepMind after requests from the scientific community.<sup id="cite_ref-ElPais_60-0" class="reference"><a href="#cite_note-ElPais-60"><span class="cite-bracket">[</span>60<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-61" class="reference"><a href="#cite_note-61"><span class="cite-bracket">[</span>61<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-HassabisTweet_210618_62-0" class="reference"><a href="#cite_note-HassabisTweet_210618-62"><span class="cite-bracket">[</span>62<span class="cite-bracket">]</span></a></sup> The source code of AlphaFold 3<sup id="cite_ref-63" class="reference"><a href="#cite_note-63"><span class="cite-bracket">[</span>63<span class="cite-bracket">]</span></a></sup> was made available for non-commercial use to the scientific community upon request in November 2024. </p> <div class="mw-heading mw-heading2"><h2 id="Database_of_protein_models_generated_by_AlphaFold">Database of protein models generated by AlphaFold</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=12" title="Edit section: Database of protein models generated by AlphaFold"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1257001546">.mw-parser-output .infobox-subbox{padding:0;border:none;margin:-3px;width:auto;min-width:100%;font-size:100%;clear:none;float:none;background-color:transparent}.mw-parser-output .infobox-3cols-child{margin:auto}.mw-parser-output .infobox .navbar{font-size:100%}@media screen{html.skin-theme-clientpref-night .mw-parser-output .infobox-full-data:not(.notheme)>div:not(.notheme)[style]{background:#1f1f23!important;color:#f8f9fa}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .infobox-full-data:not(.notheme) div:not(.notheme){background:#1f1f23!important;color:#f8f9fa}}@media(min-width:640px){body.skin--responsive .mw-parser-output .infobox-table{display:table!important}body.skin--responsive .mw-parser-output .infobox-table>caption{display:table-caption!important}body.skin--responsive .mw-parser-output .infobox-table>tbody{display:table-row-group}body.skin--responsive .mw-parser-output .infobox-table tr{display:table-row!important}body.skin--responsive .mw-parser-output .infobox-table th,body.skin--responsive .mw-parser-output .infobox-table td{padding-left:inherit;padding-right:inherit}}</style><table class="infobox vevent" style="width:"><caption class="infobox-title summary">AlphaFold Protein Structure Database</caption><tbody><tr><th colspan="2" class="infobox-header" style="background-color: lavender">Content</th></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap">Data types<br />captured</th><td class="infobox-data">protein structure prediction</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap"><a href="/wiki/Organism" title="Organism">Organisms</a></th><td class="infobox-data">all UniProt proteomes</td></tr><tr><th colspan="2" class="infobox-header" style="background-color: lavender">Contact</th></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap"><a href="/wiki/Research_center" class="mw-redirect" title="Research center">Research center</a></th><td class="infobox-data">EMBL-EBI</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap">Primary citation</th><td class="infobox-data"><sup id="cite_ref-nature20210715_6-2" class="reference"><a href="#cite_note-nature20210715-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup></td></tr><tr><th colspan="2" class="infobox-header" style="background-color: lavender">Access</th></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap">Website</th><td class="infobox-data"><a rel="nofollow" class="external free" href="https://www.alphafold.ebi.ac.uk/">https://www.alphafold.ebi.ac.uk/</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap">Download URL</th><td class="infobox-data">yes</td></tr><tr><th colspan="2" class="infobox-header" style="background-color: lavender">Tools</th></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap"><a href="/wiki/Web_application" title="Web application">Web</a></th><td class="infobox-data">yes</td></tr><tr><th colspan="2" class="infobox-header" style="background-color: lavender">Miscellaneous</th></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap"><a href="/wiki/Software_license" title="Software license">License</a></th><td class="infobox-data"><a href="/wiki/CC-BY_4.0" class="mw-redirect" title="CC-BY 4.0">CC-BY 4.0</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap">Curation policy</th><td class="infobox-data">automatic</td></tr></tbody></table> <p>The <b>AlphaFold Protein Structure Database</b>, a joint project between AlphaFold and <a href="/wiki/EMBL-EBI" class="mw-redirect" title="EMBL-EBI">EMBL-EBI</a>, was launched on July 22, 2021. At launch, the database contained AlphaFold-predicted <a href="/wiki/Protein_structure_prediction" title="Protein structure prediction">models</a> for nearly the complete <a href="/wiki/UniProt" title="UniProt">UniProt</a> <a href="/wiki/Proteome" title="Proteome">proteome</a> of humans and 20 <a href="/wiki/Model_organisms" class="mw-redirect" title="Model organisms">model organisms</a>, totaling over 365,000 proteins. The database does not include proteins with fewer than 16 or more than 2700 <a href="/wiki/Amino_acid_residues" class="mw-redirect" title="Amino acid residues">amino acid residues</a>,<sup id="cite_ref-64" class="reference"><a href="#cite_note-64"><span class="cite-bracket">[</span>64<span class="cite-bracket">]</span></a></sup> but for humans they are available in the whole batch file.<sup id="cite_ref-65" class="reference"><a href="#cite_note-65"><span class="cite-bracket">[</span>65<span class="cite-bracket">]</span></a></sup> AlphaFold's initial goal (as of early 2022) was to expand the database to cover most of the UniRef90 set, which contains over 100 million proteins. As of May 15, 2022, the database contained 992,316 predictions.<sup id="cite_ref-66" class="reference"><a href="#cite_note-66"><span class="cite-bracket">[</span>66<span class="cite-bracket">]</span></a></sup> </p><p>In July 2021, UniProt-KB and <a href="/wiki/InterPro" title="InterPro">InterPro</a><sup id="cite_ref-67" class="reference"><a href="#cite_note-67"><span class="cite-bracket">[</span>67<span class="cite-bracket">]</span></a></sup> has been updated to show AlphaFold predictions when available.<sup id="cite_ref-68" class="reference"><a href="#cite_note-68"><span class="cite-bracket">[</span>68<span class="cite-bracket">]</span></a></sup> </p><p>On July 28, 2022, the team uploaded to the database the structures of around 200 million proteins from 1 million species, covering nearly every known protein on the planet.<sup id="cite_ref-69" class="reference"><a href="#cite_note-69"><span class="cite-bracket">[</span>69<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Limitations">Limitations</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=13" title="Edit section: Limitations"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>AlphaFold has various limitations: </p> <ul><li>AlphaFold DB provides models of individual protein chains (monomers), rather than their biologically relevant <a href="/wiki/Protein_complexes" class="mw-redirect" title="Protein complexes">complexes.</a><sup id="cite_ref-DB-Limitation_70-0" class="reference"><a href="#cite_note-DB-Limitation-70"><span class="cite-bracket">[</span>70<span class="cite-bracket">]</span></a></sup></li> <li>Many protein regions are predicted with low confidence score, including the <a href="/wiki/Intrinsically_disordered_protein" class="mw-redirect" title="Intrinsically disordered protein">intrinsically disordered protein</a> regions.<sup id="cite_ref-71" class="reference"><a href="#cite_note-71"><span class="cite-bracket">[</span>71<span class="cite-bracket">]</span></a></sup></li> <li>Alphafold-2 was validated for predicting structural effects of mutations with a limited success.<sup id="cite_ref-:1_72-0" class="reference"><a href="#cite_note-:1-72"><span class="cite-bracket">[</span>72<span class="cite-bracket">]</span></a></sup></li> <li>The model relies, to some extent, on co-evolutionary information from similar proteins. Therefore, it may not perform as well on synthetic proteins or proteins with very low homology to those in the training database.<sup id="cite_ref-73" class="reference"><a href="#cite_note-73"><span class="cite-bracket">[</span>73<span class="cite-bracket">]</span></a></sup></li> <li>The model's ability to predict multiple <a href="/wiki/Native_state" title="Native state">native</a> conformations of proteins is limited.</li> <li>AlphaFold 3 version can predict structures of protein complexes with a very limited set of selected <a href="/wiki/Cofactor_(biochemistry)" title="Cofactor (biochemistry)">cofactors</a> and co- and <a href="/wiki/Post-translational_modification" title="Post-translational modification">post-translational modifications</a>.<sup id="cite_ref-auto_74-0" class="reference"><a href="#cite_note-auto-74"><span class="cite-bracket">[</span>74<span class="cite-bracket">]</span></a></sup> Between 50% and 70% of the structures of the human proteome are incomplete without covalently-attached glycans.<sup id="cite_ref-75" class="reference"><a href="#cite_note-75"><span class="cite-bracket">[</span>75<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-DB-Limitation_70-1" class="reference"><a href="#cite_note-DB-Limitation-70"><span class="cite-bracket">[</span>70<span class="cite-bracket">]</span></a></sup> AlphaFill, a derived database, adds cofactors to AlphaFold models where appropriate.<sup id="cite_ref-76" class="reference"><a href="#cite_note-76"><span class="cite-bracket">[</span>76<span class="cite-bracket">]</span></a></sup></li> <li>In the algorithm, the residues are moved freely, without any restraints. Therefore, during modeling the integrity of the chain is not maintained. As a result, AlphaFold may produce topologically wrong results, like structures with an arbitrary number of knots.<sup id="cite_ref-77" class="reference"><a href="#cite_note-77"><span class="cite-bracket">[</span>77<span class="cite-bracket">]</span></a></sup></li></ul> <div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=14" title="Edit section: Applications"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>AlphaFold has been used to predict structures of proteins of <a href="/wiki/Severe_acute_respiratory_syndrome_coronavirus_2" class="mw-redirect" title="Severe acute respiratory syndrome coronavirus 2">SARS-CoV-2</a>, the causative agent of <a href="/wiki/COVID-19_pandemic" title="COVID-19 pandemic">COVID-19</a>. The structures of these proteins were pending experimental detection in early 2020.<sup id="cite_ref-78" class="reference"><a href="#cite_note-78"><span class="cite-bracket">[</span>78<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:5_8-5" class="reference"><a href="#cite_note-:5-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> Results were reviewed by scientists at the <a href="/wiki/Francis_Crick_Institute" title="Francis Crick Institute">Francis Crick Institute</a> in the United Kingdom before being released to the broader research community. The team also confirmed accurate prediction against the experimentally determined SARS-CoV-2 <a href="/wiki/Coronavirus_spike_protein" title="Coronavirus spike protein">spike protein</a> that was shared in the <a href="/wiki/Protein_Data_Bank" title="Protein Data Bank">Protein Data Bank</a>, an international open-access database, before releasing the computationally determined structures of the under-studied protein molecules.<sup id="cite_ref-:6_79-0" class="reference"><a href="#cite_note-:6-79"><span class="cite-bracket">[</span>79<span class="cite-bracket">]</span></a></sup> The team acknowledged that although these protein structures might not be the subject of ongoing therapeutical research efforts, they will add to the community's understanding of the SARS-CoV-2 virus.<sup id="cite_ref-:6_79-1" class="reference"><a href="#cite_note-:6-79"><span class="cite-bracket">[</span>79<span class="cite-bracket">]</span></a></sup> Specifically, AlphaFold 2's prediction of the structure of the <i><a href="/wiki/ORF3a" title="ORF3a">ORF3a</a></i> protein was very similar to the structure determined by researchers at <a href="/wiki/University_of_California,_Berkeley" title="University of California, Berkeley">University of California, Berkeley</a> using <a href="/wiki/Cryo-Electron_Microscopy" class="mw-redirect" title="Cryo-Electron Microscopy">cryo-electron microscopy</a>. This specific protein is believed to assist the virus in breaking out of the host cell once it replicates. This protein is also believed to play a role in triggering the inflammatory response to the infection.<sup id="cite_ref-80" class="reference"><a href="#cite_note-80"><span class="cite-bracket">[</span>80<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Published_works">Published works</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=15" title="Edit section: Published works"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li>Andrew W. Senior <i>et al.</i> (December 2019), <a rel="nofollow" class="external text" href="https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25834">"Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)"</a>, <i>Proteins: Structure, Function, Bioinformatics</i> <b>87</b>(12) 1141–1148 <style data-mw-deduplicate="TemplateStyles:r1238218222">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/a/aa/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/4/4c/Wikisource-logo.svg")right 0.1em center/12px no-repeat}body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-free a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-limited a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-registration a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-subscription a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .cs1-ws-icon a{background-size:contain;padding:0 1em 0 0}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:var(--color-error,#d33)}.mw-parser-output .cs1-visible-error{color:var(--color-error,#d33)}.mw-parser-output .cs1-maint{display:none;color:#085;margin-left:0.3em}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}@media screen{.mw-parser-output .cs1-format{font-size:95%}html.skin-theme-clientpref-night .mw-parser-output .cs1-maint{color:#18911f}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .cs1-maint{color:#18911f}}</style><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1002%2Fprot.25834">10.1002/prot.25834</a></li> <li>Andrew W. Senior <i>et al.</i> (15 January 2020), <a rel="nofollow" class="external text" href="https://www.nature.com/articles/s41586-019-1923-7">"Improved protein structure prediction using potentials from deep learning"</a>, <i><a href="/wiki/Nature_(magazine)" class="mw-redirect" title="Nature (magazine)">Nature</a></i> <b>577</b> 706–710 <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41586-019-1923-7">10.1038/s41586-019-1923-7</a></li> <li>John Jumper <i>et al.</i> (December 2020), "High Accuracy Protein Structure Prediction Using Deep Learning", in <i><a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf">Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book)</a></i>, pp. 22–24</li> <li>John Jumper <i>et al.</i> (December 2020), "<a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">AlphaFold 2</a>". Presentation given at CASP 14.</li></ul> <div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=16" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1184024115">.mw-parser-output .div-col{margin-top:0.3em;column-width:30em}.mw-parser-output .div-col-small{font-size:90%}.mw-parser-output .div-col-rules{column-rule:1px solid #aaa}.mw-parser-output .div-col dl,.mw-parser-output .div-col ol,.mw-parser-output .div-col ul{margin-top:0}.mw-parser-output .div-col li,.mw-parser-output .div-col dd{page-break-inside:avoid;break-inside:avoid-column}</style><div class="div-col" style="column-width: 30em;"> <ul><li><a href="/wiki/Folding@home" title="Folding@home">Folding@home</a></li> <li><a href="/wiki/IBM_Blue_Gene" title="IBM Blue Gene">IBM Blue Gene</a></li> <li><a href="/wiki/Foldit" title="Foldit">Foldit</a></li> <li><a href="/wiki/Rosetta@home" title="Rosetta@home">Rosetta@home</a></li> <li><a href="/wiki/Human_Proteome_Folding_Project" title="Human Proteome Folding Project">Human Proteome Folding Project</a></li> <li><a href="/wiki/AlphaZero" title="AlphaZero">AlphaZero</a></li> <li><a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a></li> <li><a href="/wiki/AlphaGeometry" title="AlphaGeometry">AlphaGeometry</a></li> <li><a href="/wiki/Predicted_Aligned_Error" title="Predicted Aligned Error">Predicted Aligned Error</a></li></ul> </div> <div class="mw-heading mw-heading2"><h2 id="References">References</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=17" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist"> <div class="mw-references-wrap mw-references-columns"><ol class="references"> <li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://deepmind.com/research/case-studies/alphafold">"AlphaFold"</a>. <i>Deepmind</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20210119220351/https://deepmind.com/research/case-studies/alphafold">Archived</a> from the original on 19 January 2021<span class="reference-accessdate">. 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Service, <a rel="nofollow" class="external text" href="https://www.science.org/content/article/game-has-changed-ai-triumphs-solving-protein-structures">'The game has changed.' AI triumphs at solving protein structures</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20230624175710/https://www.science.org/content/article/game-has-changed-ai-triumphs-solving-protein-structures">Archived</a> 2023-06-24 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/Science_(magazine)" class="mw-redirect" title="Science (magazine)">Science</a></i>, 30 November 2020</span> </li> <li id="cite_note-nature20210715-6"><span class="mw-cite-backlink">^ <a href="#cite_ref-nature20210715_6-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-nature20210715_6-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-nature20210715_6-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFJumperEvansPritzelGreen2021" class="citation journal cs1">Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ; Žídek, Augustin; Potapenko, Anna; Bridgland, Alex; Meyer, Clemens; Kohl, Simon A A; Ballard, Andrew J; Cowie, Andrew; Romera-Paredes, Bernardino; Nikolov, Stanislav; Jain, Rishub; Adler, Jonas; Back, Trevor; Petersen, Stig; Reiman, David; Clancy, Ellen; Zielinski, Michal; Steinegger, Martin; Pacholska, Michalina; Berghammer, Tamas; Bodenstein, Sebastian; Silver, David; Vinyals, Oriol; Senior, Andrew W; Kavukcuoglu, Koray; Kohli, Pushmeet; Hassabis, Demis (2021-07-15). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605">"Highly accurate protein structure prediction with AlphaFold"</a>. <i>Nature</i>. <b>596</b> (7873): <span class="nowrap">583–</span>589. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2021Natur.596..583J">2021Natur.596..583J</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41586-021-03819-2">10.1038/s41586-021-03819-2</a></span>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605">8371605</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/34265844">34265844</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature&rft.atitle=Highly+accurate+protein+structure+prediction+with+AlphaFold&rft.volume=596&rft.issue=7873&rft.pages=%3Cspan+class%3D%22nowrap%22%3E583-%3C%2Fspan%3E589&rft.date=2021-07-15&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8371605%23id-name%3DPMC&rft_id=info%3Apmid%2F34265844&rft_id=info%3Adoi%2F10.1038%2Fs41586-021-03819-2&rft_id=info%3Abibcode%2F2021Natur.596..583J&rft.aulast=Jumper&rft.aufirst=John&rft.au=Evans%2C+Richard&rft.au=Pritzel%2C+Alexander&rft.au=Green%2C+Tim&rft.au=Figurnov%2C+Michael&rft.au=Ronneberger%2C+Olaf&rft.au=Tunyasuvunakool%2C+Kathryn&rft.au=Bates%2C+Russ&rft.au=%C5%BD%C3%ADdek%2C+Augustin&rft.au=Potapenko%2C+Anna&rft.au=Bridgland%2C+Alex&rft.au=Meyer%2C+Clemens&rft.au=Kohl%2C+Simon+A+A&rft.au=Ballard%2C+Andrew+J&rft.au=Cowie%2C+Andrew&rft.au=Romera-Paredes%2C+Bernardino&rft.au=Nikolov%2C+Stanislav&rft.au=Jain%2C+Rishub&rft.au=Adler%2C+Jonas&rft.au=Back%2C+Trevor&rft.au=Petersen%2C+Stig&rft.au=Reiman%2C+David&rft.au=Clancy%2C+Ellen&rft.au=Zielinski%2C+Michal&rft.au=Steinegger%2C+Martin&rft.au=Pacholska%2C+Michalina&rft.au=Berghammer%2C+Tamas&rft.au=Bodenstein%2C+Sebastian&rft.au=Silver%2C+David&rft.au=Vinyals%2C+Oriol&rft.au=Senior%2C+Andrew+W&rft.au=Kavukcuoglu%2C+Koray&rft.au=Kohli%2C+Pushmeet&rft.au=Hassabis%2C+Demis&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8371605&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-AlQuraishiTweet-7"><span class="mw-cite-backlink">^ <a href="#cite_ref-AlQuraishiTweet_7-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-AlQuraishiTweet_7-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-AlQuraishiTweet_7-2"><sup><i><b>c</b></i></sup></a> <a href="#cite_ref-AlQuraishiTweet_7-3"><sup><i><b>d</b></i></sup></a> <a href="#cite_ref-AlQuraishiTweet_7-4"><sup><i><b>e</b></i></sup></a> <a href="#cite_ref-AlQuraishiTweet_7-5"><sup><i><b>f</b></i></sup></a></span> <span class="reference-text">Mohammed AlQuraishi, <a rel="nofollow" class="external text" href="https://twitter.com/MoAlQuraishi/status/1333383634649313280">CASP14 scores just came out and they're astounding</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220804024406/https://twitter.com/MoAlQuraishi/status/1333383634649313280">Archived</a> 2022-08-04 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, Twitter, 30 November 2020.</span> </li> <li id="cite_note-:5-8"><span class="mw-cite-backlink">^ <a href="#cite_ref-:5_8-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:5_8-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-:5_8-2"><sup><i><b>c</b></i></sup></a> <a href="#cite_ref-:5_8-3"><sup><i><b>d</b></i></sup></a> <a href="#cite_ref-:5_8-4"><sup><i><b>e</b></i></sup></a> <a href="#cite_ref-:5_8-5"><sup><i><b>f</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFCallaway2020" class="citation journal cs1">Callaway, Ewen (2020-11-30). 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Retrieved <span class="nowrap">2021-07-24</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=GitHub&rft.atitle=GitHub+-+deepmind%2Falphafold%3A+Open+source+code+for+AlphaFold.&rft_id=https%3A%2F%2Fgithub.com%2Fdeepmind%2Falphafold&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-12"><span class="mw-cite-backlink"><b><a href="#cite_ref-12">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://alphafold.ebi.ac.uk/">"AlphaFold Protein Structure Database"</a>. <i>alphafold.ebi.ac.uk</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20210724013505/https://alphafold.ebi.ac.uk/">Archived</a> from the original on 2021-07-24<span class="reference-accessdate">. Retrieved <span class="nowrap">2021-07-24</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=alphafold.ebi.ac.uk&rft.atitle=AlphaFold+Protein+Structure+Database&rft_id=https%3A%2F%2Falphafold.ebi.ac.uk%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-13"><span class="mw-cite-backlink"><b><a href="#cite_ref-13">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://scholar.google.com/scholar?cites=6286436358625670901&as_sdt=2005&sciodt=0,5&hl=en">"Google Scholar"</a>. <i>scholar.google.com</i><span class="reference-accessdate">. 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Retrieved <span class="nowrap">2024-05-09</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Google&rft.atitle=AlphaFold+3+predicts+the+structure+and+interactions+of+all+of+life%27s+molecules&rft.date=2024-05-08&rft_id=https%3A%2F%2Fblog.google%2Ftechnology%2Fai%2Fgoogle-deepmind-isomorphic-alphafold-3-ai-model%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-15"><span class="mw-cite-backlink"><b><a href="#cite_ref-15">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://www.chemh.com/beyond-alphafold-3-navigating-future-challenges-in-protein-structure-prediction/">"Beyond AlphaFold 3: Navigating Future Challenges in Protein Structure Prediction"</a>. 2024-05-10<span class="reference-accessdate">. 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The Royal Swedish Academy of Sciences. 9 October 2024<span class="reference-accessdate">. Retrieved <span class="nowrap">29 November</span> 2024</span>. <q>The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2024 with one half to David Baker..."for computational protein design" and the other half jointly to Demis Hassabis... John Jumper..."for protein structure prediction"<span class="cs1-kern-right"></span></q></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=unknown&rft.btitle=Press+release%3A+The+Nobel+Prize+in+Chemistry+2024&rft.pub=The+Royal+Swedish+Academy+of+Sciences&rft.date=2024-10-09&rft_id=https%3A%2F%2Fwww.nobelprize.org%2Fprizes%2Fchemistry%2F2024%2Fpress-release%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-:4-17"><span class="mw-cite-backlink">^ <a href="#cite_ref-:4_17-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:4_17-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFHunt2024" class="citation news cs1">Hunt, Christian Edwards, Katie (9 October 2024). <a rel="nofollow" class="external text" href="https://edition.cnn.com/2024/10/09/science/nobel-prize-chemistry-proteins-baker-hassabis-jumper-intl/index.html">"Scientists who used AI to 'crack the code' of almost all proteins win Nobel Prize in chemistry"</a>. <i>CNN</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20241010043705/https://edition.cnn.com/2024/10/09/science/nobel-prize-chemistry-proteins-baker-hassabis-jumper-intl/index.html">Archived</a> from the original on 10 October 2024<span class="reference-accessdate">. Retrieved <span class="nowrap">9 October</span> 2024</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=CNN&rft.atitle=Scientists+who+used+AI+to+%E2%80%98crack+the+code%E2%80%99+of+almost+all+proteins+win+Nobel+Prize+in+chemistry&rft.date=2024-10-09&rft.aulast=Hunt&rft.aufirst=Christian+Edwards%2C+Katie&rft_id=https%3A%2F%2Fedition.cnn.com%2F2024%2F10%2F09%2Fscience%2Fnobel-prize-chemistry-proteins-baker-hassabis-jumper-intl%2Findex.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span><span class="cs1-maint citation-comment"><code class="cs1-code">{{<a href="/wiki/Template:Cite_news" title="Template:Cite news">cite news</a>}}</code>: CS1 maint: multiple names: authors list (<a href="/wiki/Category:CS1_maint:_multiple_names:_authors_list" title="Category:CS1 maint: multiple names: authors list">link</a>)</span></span> </li> <li id="cite_note-:7-18"><span class="mw-cite-backlink">^ <a href="#cite_ref-:7_18-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:7_18-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFKnapp" class="citation web cs1">Knapp, Alex. <a rel="nofollow" class="external text" href="https://www.forbes.com/sites/alexknapp/2022/09/22/2023-breakthrough-prizes-announced-deepminds-protein-folders-awarded-3-million/">"2023 Breakthrough Prizes Announced: Deepmind's Protein Folders Awarded $3 Million"</a>. <i>Forbes</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20240509171525/https://www.forbes.com/sites/alexknapp/2022/09/22/2023-breakthrough-prizes-announced-deepminds-protein-folders-awarded-3-million/">Archived</a> from the original on 2024-05-09<span class="reference-accessdate">. Retrieved <span class="nowrap">2024-05-09</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Forbes&rft.atitle=2023+Breakthrough+Prizes+Announced%3A+Deepmind%27s+Protein+Folders+Awarded+%243+Million&rft.aulast=Knapp&rft.aufirst=Alex&rft_id=https%3A%2F%2Fwww.forbes.com%2Fsites%2Falexknapp%2F2022%2F09%2F22%2F2023-breakthrough-prizes-announced-deepminds-protein-folders-awarded-3-million%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-:3-19"><span class="mw-cite-backlink">^ <a href="#cite_ref-:3_19-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:3_19-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-:3_19-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery">"AlphaFold: Using AI for scientific discovery"</a>. <i>Deepmind</i>. 15 January 2020. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220307220937/https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery">Archived</a> from the original on 2022-03-07<span class="reference-accessdate">. Retrieved <span class="nowrap">2020-11-30</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Deepmind&rft.atitle=AlphaFold%3A+Using+AI+for+scientific+discovery&rft.date=2020-01-15&rft_id=https%3A%2F%2Fdeepmind.com%2Fblog%2Farticle%2FAlphaFold-Using-AI-for-scientific-discovery&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-20"><span class="mw-cite-backlink"><b><a href="#cite_ref-20">^</a></b></span> <span class="reference-text"><a href="/w/index.php?title=Mohammed_AlQuraishi&action=edit&redlink=1" class="new" title="Mohammed AlQuraishi (page does not exist)">Mohammed AlQuraishi</a> (May 2019), <a rel="nofollow" class="external text" href="https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf">AlphaFold at CASP13</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20211122013208/https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf">Archived</a> 2021-11-22 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i>Bioinformatics</i>, <b>35</b>(22), 4862–4865 <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1093%2Fbioinformatics%2Fbtz422">10.1093/bioinformatics/btz422</a>. See also Mohammed AlQuraishi (December 9, 2018), <a rel="nofollow" class="external text" href="https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/">AlphaFold @ CASP13: "What just happened?"</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220729130717/https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/">Archived</a> 2022-07-29 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a> (blog post).<br />Mohammed AlQuraishi (15 January 2020), <a rel="nofollow" class="external text" href="https://www.nature.com/articles/d41586-019-03951-0">A watershed moment for protein structure prediction</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220623072409/https://www.nature.com/articles/d41586-019-03951-0">Archived</a> 2022-06-23 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/Nature_(journal)" title="Nature (journal)">Nature</a></i> <b>577</b>, 627–628 <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fd41586-019-03951-0">10.1038/d41586-019-03951-0</a></span> </li> <li id="cite_note-21"><span class="mw-cite-backlink"><b><a href="#cite_ref-21">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://fold.it/portal/node/2008706">AlphaFold: Machine learning for protein structure prediction</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220512085046/http://fold.it/portal/node/2008706">Archived</a> 2022-05-12 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <a href="/wiki/Foldit" title="Foldit">Foldit</a>, 31 January 2020</span> </li> <li id="cite_note-nature-22"><span class="mw-cite-backlink">^ <a href="#cite_ref-nature_22-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-nature_22-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFJumperEvansPritzelGreen2021" class="citation journal cs1">Jumper, John; et al. (August 2021). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605">"Highly accurate protein structure prediction with AlphaFold"</a>. <i>Nature</i>. <b>596</b> (7873): <span class="nowrap">583–</span>589. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2021Natur.596..583J">2021Natur.596..583J</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41586-021-03819-2">10.1038/s41586-021-03819-2</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1476-4687">1476-4687</a>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605">8371605</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/34265844">34265844</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature&rft.atitle=Highly+accurate+protein+structure+prediction+with+AlphaFold&rft.volume=596&rft.issue=7873&rft.pages=%3Cspan+class%3D%22nowrap%22%3E583-%3C%2Fspan%3E589&rft.date=2021-08&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8371605%23id-name%3DPMC&rft_id=info%3Abibcode%2F2021Natur.596..583J&rft_id=info%3Apmid%2F34265844&rft_id=info%3Adoi%2F10.1038%2Fs41586-021-03819-2&rft.issn=1476-4687&rft.aulast=Jumper&rft.aufirst=John&rft.au=Evans%2C+Richard&rft.au=Pritzel%2C+Alexander&rft.au=Green%2C+Tim&rft.au=Figurnov%2C+Michael&rft.au=Ronneberger%2C+Olaf&rft.au=Tunyasuvunakool%2C+Kathryn&rft.au=Bates%2C+Russ&rft.au=%C5%BD%C3%ADdek%2C+Augustin&rft.au=Potapenko%2C+Anna&rft.au=Bridgland%2C+Alex&rft.au=Meyer%2C+Clemens&rft.au=Kohl%2C+Simon+A.+A.&rft.au=Ballard%2C+Andrew+J.&rft.au=Cowie%2C+Andrew&rft.au=Romera-Paredes%2C+Bernardino&rft.au=Nikolov%2C+Stanislav&rft.au=Jain%2C+Rishub&rft.au=Adler%2C+Jonas&rft.au=Back%2C+Trevor&rft.au=Petersen%2C+Stig&rft.au=Reiman%2C+David&rft.au=Clancy%2C+Ellen&rft.au=Zielinski%2C+Michal&rft.au=Steinegger%2C+Martin&rft.au=Pacholska%2C+Michalina&rft.au=Berghammer%2C+Tamas&rft.au=Bodenstein%2C+Sebastian&rft.au=Silver%2C+David&rft.au=Vinyals%2C+Oriol&rft.au=Senior%2C+Andrew+W.&rft.au=Kavukcuoglu%2C+Koray&rft.au=Kohli%2C+Pushmeet&rft.au=Hassabis%2C+Demis&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8371605&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-economist20201130-23"><span class="mw-cite-backlink">^ <a href="#cite_ref-economist20201130_23-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-economist20201130_23-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation news cs1"><a rel="nofollow" class="external text" href="https://www.economist.com/science-and-technology/2020/11/30/deepmind-is-answering-one-of-biologys-biggest-challenges">"DeepMind is answering one of biology's biggest challenges"</a>. <i>The Economist</i>. 2020-11-30. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0013-0613">0013-0613</a>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20201203085450/https://www.economist.com/science-and-technology/2020/11/30/deepmind-is-answering-one-of-biologys-biggest-challenges">Archived</a> from the original on 2020-12-03<span class="reference-accessdate">. Retrieved <span class="nowrap">2020-11-30</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=The+Economist&rft.atitle=DeepMind+is+answering+one+of+biology%27s+biggest+challenges&rft.date=2020-11-30&rft.issn=0013-0613&rft_id=https%3A%2F%2Fwww.economist.com%2Fscience-and-technology%2F2020%2F11%2F30%2Fdeepmind-is-answering-one-of-biologys-biggest-challenges&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-KahnLessons-24"><span class="mw-cite-backlink">^ <a href="#cite_ref-KahnLessons_24-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-KahnLessons_24-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Jeremy Kahn, <a rel="nofollow" class="external text" href="https://fortune.com/2020/12/01/lessons-from-deepminds-a-i-breakthrough-eye-on-ai/">Lessons from DeepMind's breakthrough in protein-folding A.I.</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220408095912/https://fortune.com/2020/12/01/lessons-from-deepminds-a-i-breakthrough-eye-on-ai/">Archived</a> 2022-04-08 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/Fortune_(magazine)" title="Fortune (magazine)">Fortune</a></i>, 1 December 2020</span> </li> <li id="cite_note-block_diagram-25"><span class="mw-cite-backlink">^ <a href="#cite_ref-block_diagram_25-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-block_diagram_25-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-block_diagram_25-2"><sup><i><b>c</b></i></sup></a> <a href="#cite_ref-block_diagram_25-3"><sup><i><b>d</b></i></sup></a></span> <span class="reference-text">See block diagram. Also John Jumper <i>et al.</i> (1 December 2020), <a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">AlphaFold 2 presentation</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220703015739/https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">Archived</a> 2022-07-03 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, slide 10</span> </li> <li id="cite_note-Alpha2Abstract-26"><span class="mw-cite-backlink"><b><a href="#cite_ref-Alpha2Abstract_26-0">^</a></b></span> <span class="reference-text">John Jumper et al., conference abstract (December 2020)</span> </li> <li id="cite_note-27"><span class="mw-cite-backlink"><b><a href="#cite_ref-27">^</a></b></span> <span class="reference-text">The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper <i>et al.</i> (1 December 2020), <a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">AlphaFold 2 presentation</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220703015739/https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">Archived</a> 2022-07-03 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, slide 12).<br /> One design for a transformer network with <a href="/wiki/Euclidean_group" title="Euclidean group">SE(3)</a>-<a href="/wiki/Equivariant_map" title="Equivariant map">equivariance</a> was proposed in Fabian Fuchs <i>et al</i> <a rel="nofollow" class="external text" href="https://arxiv.org/pdf/2006.10503.pdf">SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20211007133527/https://arxiv.org/pdf/2006.10503.pdf">Archived</a> 2021-10-07 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <a href="/wiki/NeurIPS" class="mw-redirect" title="NeurIPS">NeurIPS</a> 2020; also <a rel="nofollow" class="external text" href="https://fabianfuchsml.github.io/se3transformer/">website</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220703015807/https://fabianfuchsml.github.io/se3transformer/">Archived</a> 2022-07-03 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>. It is not known how similar this may or may not be to what was used in AlphaFold.<br /> See also <a rel="nofollow" class="external text" href="https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/#s3.3">the blog post</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20201208164545/https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/#s3.3">Archived</a> 2020-12-08 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a> by AlQuaraishi on this, or the <a rel="nofollow" class="external text" href="https://fabianfuchsml.github.io/alphafold2/">more detailed post</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220703015630/https://fabianfuchsml.github.io/alphafold2/">Archived</a> 2022-07-03 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a> by Fabian Fuchs</span> </li> <li id="cite_note-AF2iterations-28"><span class="mw-cite-backlink"><b><a href="#cite_ref-AF2iterations_28-0">^</a></b></span> <span class="reference-text">John Jumper <i>et al.</i> (1 December 2020), <a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">AlphaFold 2 presentation</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220703015739/https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">Archived</a> 2022-07-03 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, slides 12 to 20</span> </li> <li id="cite_note-29"><span class="mw-cite-backlink"><b><a href="#cite_ref-29">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFCallaway2022" class="citation journal cs1">Callaway, Ewen (13 April 2022). <a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fd41586-022-00997-5">"What's next for AlphaFold and the AI protein-folding revolution"</a>. <i>Nature</i>. <b>604</b> (7905): <span class="nowrap">234–</span>238. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2022Natur.604..234C">2022Natur.604..234C</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fd41586-022-00997-5">10.1038/d41586-022-00997-5</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/35418629">35418629</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:248156195">248156195</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature&rft.atitle=What%27s+next+for+AlphaFold+and+the+AI+protein-folding+revolution&rft.volume=604&rft.issue=7905&rft.pages=%3Cspan+class%3D%22nowrap%22%3E234-%3C%2Fspan%3E238&rft.date=2022-04-13&rft_id=info%3Adoi%2F10.1038%2Fd41586-022-00997-5&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A248156195%23id-name%3DS2CID&rft_id=info%3Apmid%2F35418629&rft_id=info%3Abibcode%2F2022Natur.604..234C&rft.aulast=Callaway&rft.aufirst=Ewen&rft_id=https%3A%2F%2Fdoi.org%2F10.1038%252Fd41586-022-00997-5&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-30"><span class="mw-cite-backlink"><b><a href="#cite_ref-30">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFMetz2024" class="citation news cs1">Metz, Cade (2024-05-08). <a rel="nofollow" class="external text" href="https://www.nytimes.com/2024/05/08/technology/google-ai-molecules-alphafold3.html">"Google Unveils A.I. for Predicting Behavior of Human Molecules"</a>. <i>The New York Times</i>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0362-4331">0362-4331</a>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20241010043551/https://www.nytimes.com/2024/05/08/technology/google-ai-molecules-alphafold3.html">Archived</a> from the original on 2024-10-10<span class="reference-accessdate">. Retrieved <span class="nowrap">2024-05-09</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=The+New+York+Times&rft.atitle=Google+Unveils+A.I.+for+Predicting+Behavior+of+Human+Molecules&rft.date=2024-05-08&rft.issn=0362-4331&rft.aulast=Metz&rft.aufirst=Cade&rft_id=https%3A%2F%2Fwww.nytimes.com%2F2024%2F05%2F08%2Ftechnology%2Fgoogle-ai-molecules-alphafold3.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-31"><span class="mw-cite-backlink"><b><a href="#cite_ref-31">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFAbramsonAdlerDungerEvans2024" class="citation journal cs1">Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O’Neill, Michael; Reiman, David (2024-05-08). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168924">"Accurate structure prediction of biomolecular interactions with AlphaFold 3"</a>. <i>Nature</i>. <b>630</b> (8016): <span class="nowrap">493–</span>500. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2024Natur.630..493A">2024Natur.630..493A</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41586-024-07487-w">10.1038/s41586-024-07487-w</a></span>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1476-4687">1476-4687</a>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168924">11168924</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/38718835">38718835</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature&rft.atitle=Accurate+structure+prediction+of+biomolecular+interactions+with+AlphaFold+3&rft.volume=630&rft.issue=8016&rft.pages=%3Cspan+class%3D%22nowrap%22%3E493-%3C%2Fspan%3E500&rft.date=2024-05-08&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC11168924%23id-name%3DPMC&rft_id=info%3Abibcode%2F2024Natur.630..493A&rft_id=info%3Apmid%2F38718835&rft_id=info%3Adoi%2F10.1038%2Fs41586-024-07487-w&rft.issn=1476-4687&rft.aulast=Abramson&rft.aufirst=Josh&rft.au=Adler%2C+Jonas&rft.au=Dunger%2C+Jack&rft.au=Evans%2C+Richard&rft.au=Green%2C+Tim&rft.au=Pritzel%2C+Alexander&rft.au=Ronneberger%2C+Olaf&rft.au=Willmore%2C+Lindsay&rft.au=Ballard%2C+Andrew+J.&rft.au=Bambrick%2C+Joshua&rft.au=Bodenstein%2C+Sebastian+W.&rft.au=Evans%2C+David+A.&rft.au=Hung%2C+Chia-Chun&rft.au=O%E2%80%99Neill%2C+Michael&rft.au=Reiman%2C+David&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC11168924&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-32"><span class="mw-cite-backlink"><b><a href="#cite_ref-32">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://www.nature.com/articles/s41586-024-07487-w_reference.pdf">Accurate structure prediction of biomolecular interactions with AlphaFold 3</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20240512210527/https://www.nature.com/articles/s41586-024-07487-w_reference.pdf">Archived</a> 2024-05-12 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, pdf of preprint of the article in Nature.</span> </li> <li id="cite_note-33"><span class="mw-cite-backlink"><b><a href="#cite_ref-33">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://golgi.sandbox.google.com/about">"A non-commercial server of AlphaFold-3"</a>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20241010043551/https://golgi.sandbox.google.com/welcome">Archived</a> from the original on 2024-10-10<span class="reference-accessdate">. Retrieved <span class="nowrap">2024-05-12</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=unknown&rft.btitle=A+non-commercial+server+of+AlphaFold-3&rft_id=https%3A%2F%2Fgolgi.sandbox.google.com%2Fabout&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-34"><span class="mw-cite-backlink"><b><a href="#cite_ref-34">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://predictioncenter.org/casp13/zscores_final.cgi">Group performance based on combined z-scores</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220308055516/https://predictioncenter.org/casp13/zscores_final.cgi">Archived</a> 2022-03-08 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, CASP 13, December 2018. (AlphaFold = Team 043: A7D)</span> </li> <li id="cite_note-Guardian2018-35"><span class="mw-cite-backlink">^ <a href="#cite_ref-Guardian2018_35-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Guardian2018_35-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFSample2018" class="citation news cs1">Sample, Ian (2 December 2018). <a rel="nofollow" class="external text" href="https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins">"Google's DeepMind predicts 3D shapes of proteins"</a>. <i>The Guardian</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20190718162031/https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins">Archived</a> from the original on 18 July 2019<span class="reference-accessdate">. Retrieved <span class="nowrap">30 November</span> 2020</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=The+Guardian&rft.atitle=Google%27s+DeepMind+predicts+3D+shapes+of+proteins&rft.date=2018-12-02&rft.aulast=Sample&rft.aufirst=Ian&rft_id=https%3A%2F%2Fwww.theguardian.com%2Fscience%2F2018%2Fdec%2F02%2Fgoogle-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-36"><span class="mw-cite-backlink"><b><a href="#cite_ref-36">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://deepmind.com/blog/article/alphafold-casp13">"AlphaFold: Using AI for scientific discovery"</a>. <i>Deepmind</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20241010043555/https://deepmind.google/discover/blog/alphafold-using-ai-for-scientific-discovery/">Archived</a> from the original on 10 October 2024<span class="reference-accessdate">. Retrieved <span class="nowrap">30 November</span> 2020</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Deepmind&rft.atitle=AlphaFold%3A+Using+AI+for+scientific+discovery&rft_id=https%3A%2F%2Fdeepmind.com%2Fblog%2Farticle%2Falphafold-casp13&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-37"><span class="mw-cite-backlink"><b><a href="#cite_ref-37">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFSingh2020" class="citation journal cs1">Singh, Arunima (2020). <a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41592-020-0779-y">"Deep learning 3D structures"</a>. <i>Nature Methods</i>. <b>17</b> (3): 249. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41592-020-0779-y">10.1038/s41592-020-0779-y</a></span>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1548-7105">1548-7105</a>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/32132733">32132733</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:212403708">212403708</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature+Methods&rft.atitle=Deep+learning+3D+structures&rft.volume=17&rft.issue=3&rft.pages=249&rft.date=2020&rft.issn=1548-7105&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A212403708%23id-name%3DS2CID&rft_id=info%3Apmid%2F32132733&rft_id=info%3Adoi%2F10.1038%2Fs41592-020-0779-y&rft.aulast=Singh&rft.aufirst=Arunima&rft_id=https%3A%2F%2Fdoi.org%2F10.1038%252Fs41592-020-0779-y&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-38"><span class="mw-cite-backlink"><b><a href="#cite_ref-38">^</a></b></span> <span class="reference-text">See <a rel="nofollow" class="external text" href="https://predictioncenter.org/casp13/results.cgi?view=tb-sel">CASP 13 data tables</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220314052044/https://predictioncenter.org/casp13/results.cgi?view=tb-sel">Archived</a> 2022-03-14 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a> for 043 A7D, 322 Zhang, and 089 MULTICOM</span> </li> <li id="cite_note-39"><span class="mw-cite-backlink"><b><a href="#cite_ref-39">^</a></b></span> <span class="reference-text">Wei Zheng <i>et al</i>,<a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/31365149/">Deep-learning contact-map guided protein structure prediction in CASP13</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220122134714/https://pubmed.ncbi.nlm.nih.gov/31365149/">Archived</a> 2022-01-22 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i>Proteins: Structure, Function, and Bioinformatics</i>, <b>87</b>(12) 1149–1164 <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1002%2Fprot.25792">10.1002/prot.25792</a>; and <a rel="nofollow" class="external text" href="https://www.predictioncenter.org/CASP13/doc/presentations/Pred_CASP13_TS_YZhang-Groups_Redacted.pdf">slides</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220726235133/https://www.predictioncenter.org/CASP13/doc/presentations/Pred_CASP13_TS_YZhang-Groups_Redacted.pdf">Archived</a> 2022-07-26 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a></span> </li> <li id="cite_note-40"><span class="mw-cite-backlink"><b><a href="#cite_ref-40">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFHouWuCaoCheng2019" class="citation journal cs1">Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800999">"Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13"</a>. <i>Proteins: Structure, Function, and Bioinformatics</i>. <b>87</b> (12). Wiley: <span class="nowrap">1165–</span>1178. <a href="/wiki/BioRxiv_(identifier)" class="mw-redirect" title="BioRxiv (identifier)">bioRxiv</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1101%2F552422">10.1101/552422</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1002%2Fprot.25697">10.1002/prot.25697</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0887-3585">0887-3585</a>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800999">6800999</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/30985027">30985027</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Proteins%3A+Structure%2C+Function%2C+and+Bioinformatics&rft.atitle=Protein+tertiary+structure+modeling+driven+by+deep+learning+and+contact+distance+prediction+in+CASP13&rft.volume=87&rft.issue=12&rft.pages=%3Cspan+class%3D%22nowrap%22%3E1165-%3C%2Fspan%3E1178&rft.date=2019-04-25&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC6800999%23id-name%3DPMC&rft_id=https%3A%2F%2Fdoi.org%2F10.1101%2F552422%23id-name%3DbioRxiv&rft_id=info%3Apmid%2F30985027&rft_id=info%3Adoi%2F10.1002%2Fprot.25697&rft.issn=0887-3585&rft.aulast=Hou&rft.aufirst=Jie&rft.au=Wu%2C+Tianqi&rft.au=Cao%2C+Renzhi&rft.au=Cheng%2C+Jianlin&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC6800999&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-:2-41"><span class="mw-cite-backlink">^ <a href="#cite_ref-:2_41-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:2_41-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation news cs1"><a rel="nofollow" class="external text" href="https://www.bloomberg.com/news/articles/2020-11-30/deepmind-s-alphafold-crosses-threshold-in-solving-protein-riddle">"DeepMind Breakthrough Helps to Solve How Diseases Invade Cells"</a>. <i>Bloomberg.com</i>. 2020-11-30. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220405104958/https://www.bloomberg.com/news/articles/2020-11-30/deepmind-s-alphafold-crosses-threshold-in-solving-protein-riddle">Archived</a> from the original on 2022-04-05<span class="reference-accessdate">. Retrieved <span class="nowrap">2020-11-30</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Bloomberg.com&rft.atitle=DeepMind+Breakthrough+Helps+to+Solve+How+Diseases+Invade+Cells&rft.date=2020-11-30&rft_id=https%3A%2F%2Fwww.bloomberg.com%2Fnews%2Farticles%2F2020-11-30%2Fdeepmind-s-alphafold-crosses-threshold-in-solving-protein-riddle&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-42"><span class="mw-cite-backlink"><b><a href="#cite_ref-42">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13">"deepmind/deepmind-research"</a>. <i>GitHub</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220201014058/https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13">Archived</a> from the original on 2022-02-01<span class="reference-accessdate">. Retrieved <span class="nowrap">2020-11-30</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=GitHub&rft.atitle=deepmind%2Fdeepmind-research&rft_id=https%3A%2F%2Fgithub.com%2Fdeepmind%2Fdeepmind-research%2Ftree%2Fmaster%2Falphafold_casp13&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-DeepMindAlpha2-43"><span class="mw-cite-backlink">^ <a href="#cite_ref-DeepMindAlpha2_43-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-DeepMindAlpha2_43-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-DeepMindAlpha2_43-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology">"AlphaFold: a solution to a 50-year-old grand challenge in biology"</a>. <i>Deepmind</i>. 30 November 2020. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20201130153625/https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology">Archived</a> from the original on 30 November 2020<span class="reference-accessdate">. Retrieved <span class="nowrap">30 November</span> 2020</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Deepmind&rft.atitle=AlphaFold%3A+a+solution+to+a+50-year-old+grand+challenge+in+biology&rft.date=2020-11-30&rft_id=https%3A%2F%2Fdeepmind.com%2Fblog%2Farticle%2Falphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-44"><span class="mw-cite-backlink"><b><a href="#cite_ref-44">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://www.technologyreview.com/2020/11/30/1012712/deepmind-protein-folding-ai-solved-biology-science-drugs-disease/">"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology"</a>. <i>MIT Technology Review</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20210828104639/https://www.technologyreview.com/2020/11/30/1012712/deepmind-protein-folding-ai-solved-biology-science-drugs-disease/">Archived</a> from the original on 28 August 2021<span class="reference-accessdate">. Retrieved <span class="nowrap">30 November</span> 2020</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=MIT+Technology+Review&rft.atitle=DeepMind%27s+protein-folding+AI+has+solved+a+50-year-old+grand+challenge+of+biology&rft_id=https%3A%2F%2Fwww.technologyreview.com%2F2020%2F11%2F30%2F1012712%2Fdeepmind-protein-folding-ai-solved-biology-science-drugs-disease%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-45"><span class="mw-cite-backlink"><b><a href="#cite_ref-45">^</a></b></span> <span class="reference-text">For the GDT_TS measure used, each atom in the prediction scores a quarter of a point if it is within 8 Å (0.80 nm) of the experimental position; half a point if it is within 4 Å, three-quarters of a point if it is within 2 Å, and a whole point if it is within 1 Å.</span> </li> <li id="cite_note-46"><span class="mw-cite-backlink"><b><a href="#cite_ref-46">^</a></b></span> <span class="reference-text">To achieve a GDT_TS score of 92.5, mathematically at least 70% of the structure must be accurate to within 1 Å, and at least 85% must be accurate to within 2 Å,</span> </li> <li id="cite_note-47"><span class="mw-cite-backlink"><b><a href="#cite_ref-47">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFCallaway2022" class="citation journal cs1">Callaway, Ewen (2022-12-13). <a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fd41586-022-04438-1">"After AlphaFold: protein-folding contest seeks next big breakthrough"</a>. <i>Nature</i>. <b>613</b> (7942): <span class="nowrap">13–</span>14. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fd41586-022-04438-1">10.1038/d41586-022-04438-1</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/36513827">36513827</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:254660427">254660427</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature&rft.atitle=After+AlphaFold%3A+protein-folding+contest+seeks+next+big+breakthrough&rft.volume=613&rft.issue=7942&rft.pages=%3Cspan+class%3D%22nowrap%22%3E13-%3C%2Fspan%3E14&rft.date=2022-12-13&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A254660427%23id-name%3DS2CID&rft_id=info%3Apmid%2F36513827&rft_id=info%3Adoi%2F10.1038%2Fd41586-022-04438-1&rft.aulast=Callaway&rft.aufirst=Ewen&rft_id=https%3A%2F%2Fdoi.org%2F10.1038%252Fd41586-022-04438-1&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-CASP_release-48"><span class="mw-cite-backlink"><b><a href="#cite_ref-CASP_release_48-0">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/doc/CASP14_press_release.html">Artificial intelligence solution to a 50-year-old science challenge could 'revolutionise' medical research</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220424220605/https://predictioncenter.org/casp14/doc/CASP14_press_release.html">Archived</a> 2022-04-24 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a> (press release), <a href="/wiki/CASP" title="CASP">CASP</a> organising committee, 30 November 2020</span> </li> <li id="cite_note-49"><span class="mw-cite-backlink"><b><a href="#cite_ref-49">^</a></b></span> <span class="reference-text">Brigitte Nerlich, <a rel="nofollow" class="external text" href="https://blogs.nottingham.ac.uk/makingsciencepublic/2020/12/04/protein-folding-and-science-communication-between-hype-and-humility/">Protein folding and science communication: Between hype and humility</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220215161037/https://blogs.nottingham.ac.uk/makingsciencepublic/2020/12/04/protein-folding-and-science-communication-between-hype-and-humility/">Archived</a> 2022-02-15 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <a href="/wiki/University_of_Nottingham" title="University of Nottingham">University of Nottingham</a> blog, 4 December 2020</span> </li> <li id="cite_note-50"><span class="mw-cite-backlink"><b><a href="#cite_ref-50">^</a></b></span> <span class="reference-text">Michael Le Page, <a rel="nofollow" class="external text" href="https://www.newscientist.com/article/2261156-deepminds-ai-biologist-can-decipher-secrets-of-the-machinery-of-life/">DeepMind's AI biologist can decipher secrets of the machinery of life</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220802003315/https://www.newscientist.com/article/2261156-deepminds-ai-biologist-can-decipher-secrets-of-the-machinery-of-life/">Archived</a> 2022-08-02 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/New_Scientist" title="New Scientist">New Scientist</a></i>, 30 November 2020</span> </li> <li id="cite_note-51"><span class="mw-cite-backlink"><b><a href="#cite_ref-51">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://www.newscientist.com/article/2261613-the-predictions-of-deepminds-latest-ai-could-revolutionise-medicine/">The predictions of DeepMind's latest AI could revolutionise medicine</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20211107163155/https://www.newscientist.com/article/2261613-the-predictions-of-deepminds-latest-ai-could-revolutionise-medicine/">Archived</a> 2021-11-07 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/New_Scientist" title="New Scientist">New Scientist</a></i>, 2 December 2020</span> </li> <li id="cite_note-52"><span class="mw-cite-backlink"><b><a href="#cite_ref-52">^</a></b></span> <span class="reference-text">Tom Whipple, <a rel="nofollow" class="external text" href="https://www.thetimes.co.uk/edition/news/deepmind-finds-biology-s-holy-grail-with-answer-to-protein-problem-htg6s7qlq">Deepmind finds biology's 'holy grail' with answer to protein problem</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20210119055048/https://www.thetimes.co.uk/edition/news/deepmind-finds-biology-s-holy-grail-with-answer-to-protein-problem-htg6s7qlq">Archived</a> 2021-01-19 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/The_Times" title="The Times">The Times</a></i> (online), 30 November 2020.<br />In all science editor Tom Whipple wrote six articles on the subject for <i>The Times</i> on the day the news broke. (<a rel="nofollow" class="external text" href="https://twitter.com/whippletom/status/1333494448420958210">thread</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20211108142415/https://twitter.com/whippletom/status/1333494448420958210">Archived</a> 2021-11-08 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>).</span> </li> <li id="cite_note-53"><span class="mw-cite-backlink"><b><a href="#cite_ref-53">^</a></b></span> <span class="reference-text"><a href="/w/index.php?title=Cade_Metz&action=edit&redlink=1" class="new" title="Cade Metz (page does not exist)">Cade Metz</a>, <a rel="nofollow" class="external text" href="https://www.nytimes.com/2020/11/30/technology/deepmind-ai-protein-folding.html">London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220804151432/https://www.nytimes.com/2020/11/30/technology/deepmind-ai-protein-folding.html">Archived</a> 2022-08-04 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/New_York_Times" class="mw-redirect" title="New York Times">New York Times</a></i>, 30 November 2020</span> </li> <li id="cite_note-54"><span class="mw-cite-backlink"><b><a href="#cite_ref-54">^</a></b></span> <span class="reference-text">Ian Sample,<a rel="nofollow" class="external text" href="https://www.theguardian.com/technology/2020/nov/30/deepmind-ai-cracks-50-year-old-problem-of-biology-research">DeepMind AI cracks 50-year-old problem of protein folding</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20201130160106/https://www.theguardian.com/technology/2020/nov/30/deepmind-ai-cracks-50-year-old-problem-of-biology-research">Archived</a> 2020-11-30 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <i><a href="/wiki/The_Guardian" title="The Guardian">The Guardian</a></i>, 30 November 2020</span> </li> <li id="cite_note-55"><span class="mw-cite-backlink"><b><a href="#cite_ref-55">^</a></b></span> <span class="reference-text">Lizzie Roberts, <a rel="nofollow" class="external text" href="https://www.telegraph.co.uk/news/2020/11/30/google-ai-researchers-crack-50-year-old-protein-folding-problem/">'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220804003744/https://www.telegraph.co.uk/news/2020/11/30/google-ai-researchers-crack-50-year-old-protein-folding-problem/">Archived</a> 2022-08-04 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>. <i><a href="/wiki/Daily_Telegraph" class="mw-redirect" title="Daily Telegraph">Daily Telegraph</a></i>, 30 November 2020</span> </li> <li id="cite_note-56"><span class="mw-cite-backlink"><b><a href="#cite_ref-56">^</a></b></span> <span class="reference-text"><a href="/wiki/Tim_Hubbard" title="Tim Hubbard">Tim Hubbard</a>, <a rel="nofollow" class="external text" href="https://timjph.medium.com/the-secret-of-life-part-2-the-solution-of-the-protein-folding-problem-c544f3a77ee3">The secret of life, part 2: the solution of the protein folding problem.</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220514070022/https://timjph.medium.com/the-secret-of-life-part-2-the-solution-of-the-protein-folding-problem-c544f3a77ee3">Archived</a> 2022-05-14 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <a href="/wiki/Medium.com" class="mw-redirect" title="Medium.com">medium.com</a>, 30 November 2020</span> </li> <li id="cite_note-57"><span class="mw-cite-backlink"><b><a href="#cite_ref-57">^</a></b></span> <span class="reference-text">e.g. Greg Bowman, <a rel="nofollow" class="external text" href="https://foldingathome.org/2020/12/08/protein-folding-and-related-problems-remain-unsolved-despite-alphafolds-advance/">Protein folding and related problems remain unsolved despite AlphaFold's advance</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220713073427/https://foldingathome.org/2020/12/08/protein-folding-and-related-problems-remain-unsolved-despite-alphafolds-advance/">Archived</a> 2022-07-13 at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>, <a href="/wiki/Folding@home" title="Folding@home">Folding@home</a> blog, 8 December 2020</span> </li> <li id="cite_note-58"><span class="mw-cite-backlink"><b><a href="#cite_ref-58">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFSample2023" class="citation news cs1">Sample, Ian (2023-09-21). <a rel="nofollow" class="external text" href="https://www.theguardian.com/science/2023/sep/21/team-behind-ai-program-alphafold-win-lasker-science-prize">"Team behind AI program AlphaFold win Lasker science prize"</a>. <i>The Guardian</i>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0261-3077">0261-3077</a>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20241010043702/https://www.theguardian.com/science/2023/sep/21/team-behind-ai-program-alphafold-win-lasker-science-prize">Archived</a> from the original on 2024-10-10<span class="reference-accessdate">. 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Retrieved <span class="nowrap">27 July</span> 2021</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=alphafold.ebi.ac.uk&rft.atitle=AlphaFold+Protein+Structure+Database&rft_id=https%3A%2F%2Falphafold.ebi.ac.uk%2Fdownload&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-66"><span class="mw-cite-backlink"><b><a href="#cite_ref-66">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://www.alphafold.ebi.ac.uk/">"AlphaFold Protein Structure Database"</a>. <i>www.alphafold.ebi.ac.uk</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220802094731/https://www.alphafold.ebi.ac.uk/">Archived</a> from the original on 2022-08-02<span class="reference-accessdate">. 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Retrieved <span class="nowrap">2020-12-01</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Wired&rft.atitle=AI+Can+Help+Scientists+Find+a+Covid-19+Vaccine&rft.issn=1059-1028&rft_id=https%3A%2F%2Fwww.wired.com%2Fstory%2Fopinion-ai-can-help-find-scientists-find-a-covid-19-vaccine%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-:6-79"><span class="mw-cite-backlink">^ <a href="#cite_ref-:6_79-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:6_79-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19">"Computational predictions of protein structures associated with COVID-19"</a>. <i>Deepmind</i>. 4 August 2020. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20220325185435/https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19">Archived</a> from the original on 2022-03-25<span class="reference-accessdate">. Retrieved <span class="nowrap">2020-12-01</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Deepmind&rft.atitle=Computational+predictions+of+protein+structures+associated+with+COVID-19&rft.date=2020-08-04&rft_id=https%3A%2F%2Fdeepmind.com%2Fresearch%2Fopen-source%2Fcomputational-predictions-of-protein-structures-associated-with-COVID-19&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> <li id="cite_note-80"><span class="mw-cite-backlink"><b><a href="#cite_ref-80">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://fortune.com/2020/11/30/covid-protein-folding-deepmind-ai/">"How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic"</a>. <i>Fortune</i>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20201130204444/https://fortune.com/2020/11/30/covid-protein-folding-deepmind-ai/">Archived</a> from the original on 2020-11-30<span class="reference-accessdate">. Retrieved <span class="nowrap">2020-12-01</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Fortune&rft.atitle=How+DeepMind%27s+new+protein-folding+A.I.+is+already+helping+to+combat+the+coronavirus+pandemic.&rft_id=https%3A%2F%2Ffortune.com%2F2020%2F11%2F30%2Fcovid-protein-folding-deepmind-ai%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span></span> </li> </ol></div></div> <div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=18" title="Edit section: Further reading"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li>Carlos Outeiral, <a rel="nofollow" class="external text" href="https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/">CASP14: what Google DeepMind's AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics</a>, Oxford Protein Informatics Group. (3 December)</li> <li>Mohammed AlQuraishi, <a rel="nofollow" class="external text" href="https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/">AlphaFold2 @ CASP14: "It feels like one's child has left home."</a> (blog), 8 December 2020</li> <li>Mohammed AlQuraishi, <a rel="nofollow" class="external text" href="https://moalquraishi.wordpress.com/2021/07/25/the-alphafold2-method-paper-a-fount-of-good-ideas/">The AlphaFold2 Method Paper: A Fount of Good Ideas</a> (blog), 25 July 2021</li></ul> <div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=AlphaFold&action=edit&section=19" title="Edit section: External links"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a rel="nofollow" class="external text" href="https://golgi.sandbox.google.com/about">AlphaFold-3 web server</a></li> <li><a rel="nofollow" class="external text" href="https://github.com/deepmind/alphafold">AlphaFold v2.1 code and links to model</a> on <a href="/wiki/GitHub" title="GitHub">GitHub</a></li> <li><a rel="nofollow" class="external text" href="https://alphafold.ebi.ac.uk/">Open access to protein structure predictions for the human proteome and 20 other key organisms</a> at <a href="/wiki/European_Bioinformatics_Institute" title="European Bioinformatics Institute">European Bioinformatics Institute</a> (AlphaFold Protein Structure Database)</li> <li><a rel="nofollow" class="external text" href="https://predictioncenter.org/casp14/index.cgi">CASP 14</a> website</li> <li><a rel="nofollow" class="external text" href="https://www.youtube.com/watch?v=gg7WjuFs8F4">AlphaFold: The making of a scientific breakthrough</a>, DeepMind, via YouTube.</li> <li><a rel="nofollow" class="external text" href="https://github.com/sokrypton/ColabFold">ColabFold</a> (<link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFMirditaSchützeMoriwakiHeo2022" class="citation journal cs1">Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin (2022-05-30). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184281">"ColabFold: Making protein folding accessible to all"</a>. <i>Nature Methods</i>. <b>19</b> (6): <span class="nowrap">679–</span>682. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41592-022-01488-1">10.1038/s41592-022-01488-1</a></span>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184281">9184281</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/35637307">35637307</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature+Methods&rft.atitle=ColabFold%3A+Making+protein+folding+accessible+to+all&rft.volume=19&rft.issue=6&rft.pages=%3Cspan+class%3D%22nowrap%22%3E679-%3C%2Fspan%3E682&rft.date=2022-05-30&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC9184281%23id-name%3DPMC&rft_id=info%3Apmid%2F35637307&rft_id=info%3Adoi%2F10.1038%2Fs41592-022-01488-1&rft.aulast=Mirdita&rft.aufirst=Milot&rft.au=Sch%C3%BCtze%2C+Konstantin&rft.au=Moriwaki%2C+Yoshitaka&rft.au=Heo%2C+Lim&rft.au=Ovchinnikov%2C+Sergey&rft.au=Steinegger%2C+Martin&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC9184281&rfr_id=info%3Asid%2Fen.wikipedia.org%3AAlphaFold" class="Z3988"></span>), <a rel="nofollow" class="external text" href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb">version</a> for homooligomeric prediction and complexes</li></ul> <div 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class="navbox-group" style="width:1%">AlphaGo</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Versions</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a> (2015)</li> <li><a href="/wiki/Master_(software)" title="Master (software)">Master</a> (2016)</li> <li><a href="/wiki/AlphaGo_Zero" title="AlphaGo Zero">AlphaGo Zero</a> (2017)</li> <li><a href="/wiki/AlphaZero" title="AlphaZero">AlphaZero</a> (2017)</li> <li><a href="/wiki/MuZero" title="MuZero">MuZero</a> (2019)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Competitions</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo_versus_Fan_Hui" title="AlphaGo versus Fan Hui">Fan Hui</a> (2015)</li> <li><a href="/wiki/AlphaGo_versus_Lee_Sedol" title="AlphaGo versus Lee Sedol">Lee Sedol</a> (2016)</li> <li><a href="/wiki/AlphaGo_versus_Ke_Jie" title="AlphaGo versus Ke Jie">Ke Jie</a> (2017)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">In popular culture</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><i><a href="/wiki/AlphaGo_(film)" title="AlphaGo (film)">AlphaGo</a></i> (2017)</li> <li><i><a href="/wiki/The_MANIAC" title="The MANIAC">The MANIAC</a></i> (2023)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a class="mw-selflink selflink">AlphaFold</a> (2018)</li> <li><a href="/wiki/AlphaStar_(software)" title="AlphaStar (software)">AlphaStar</a> (2019)</li> <li><a href="/wiki/AlphaDev" title="AlphaDev">AlphaDev</a> (2023)</li> <li><a href="/wiki/AlphaGeometry" title="AlphaGeometry">AlphaGeometry</a> (2024)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Machine learning</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Neural networks</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Inception_(deep_learning_architecture)" title="Inception (deep learning architecture)">Inception</a> (2014)</li> <li><a href="/wiki/WaveNet" title="WaveNet">WaveNet</a> (2016)</li> <li><a href="/wiki/MobileNet" title="MobileNet">MobileNet</a> (2017)</li> <li><a href="/wiki/Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a> (2017)</li> <li><a href="/wiki/EfficientNet" title="EfficientNet">EfficientNet</a> (2019)</li> <li><a href="/wiki/Gato_(DeepMind)" title="Gato (DeepMind)">Gato</a> (2022)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Quantum_Artificial_Intelligence_Lab" title="Quantum Artificial Intelligence Lab">Quantum Artificial Intelligence Lab</a></li> <li><a href="/wiki/TensorFlow" title="TensorFlow">TensorFlow</a></li> <li><a href="/wiki/Tensor_Processing_Unit" title="Tensor Processing Unit">Tensor Processing Unit</a></li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Generative AI</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Chatbots</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Google_Assistant" title="Google Assistant">Assistant</a> (2016)</li> <li><a href="/wiki/Sparrow_(chatbot)" title="Sparrow (chatbot)">Sparrow</a> (2022)</li> <li><a href="/wiki/Gemini_(chatbot)" title="Gemini (chatbot)">Gemini</a> (2023)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Language models</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/BERT_(language_model)" title="BERT (language model)">BERT</a> (2018)</li> <li><a href="/wiki/XLNet" title="XLNet">XLNet</a> (2019)</li> <li><a href="/wiki/T5_(language_model)" title="T5 (language model)">T5</a> (2019)</li> <li><a href="/wiki/LaMDA" title="LaMDA">LaMDA</a> (2021)</li> <li><a href="/wiki/Chinchilla_(language_model)" title="Chinchilla (language model)">Chinchilla</a> (2022)</li> <li><a href="/wiki/PaLM" title="PaLM">PaLM</a> (2022)</li> <li><a href="/wiki/Gemini_(language_model)" title="Gemini (language model)">Gemini</a> (2023)</li> <li><a href="/wiki/VideoPoet" title="VideoPoet">VideoPoet</a> (2024)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/DreamBooth" title="DreamBooth">DreamBooth</a> (2022)</li> <li><a href="/wiki/NotebookLM" title="NotebookLM">NotebookLM</a> (2023)</li> <li><a href="/wiki/Google_Vids" title="Google Vids">Vids</a> (2024)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">See also</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li>"<a href="/wiki/Attention_Is_All_You_Need" title="Attention Is All You Need">Attention Is All You Need</a>"</li> <li><a href="/wiki/Future_of_Go_Summit" title="Future of Go Summit">Future of Go Summit</a></li> <li><a href="/wiki/Generative_pre-trained_transformer" title="Generative pre-trained transformer">Generative pre-trained transformer</a></li> <li><a href="/wiki/Google_Labs" title="Google Labs">Google Labs</a></li> <li><a href="/wiki/Google_Pixel" title="Google Pixel">Google Pixel</a></li> <li><a href="/wiki/Google_Workspace" title="Google Workspace">Google Workspace</a></li> <li><a href="/wiki/Robot_Constitution" title="Robot Constitution">Robot Constitution</a></li></ul> </div></td></tr><tr><td class="navbox-abovebelow" colspan="2"><div> <ul><li><span class="noviewer" typeof="mw:File"><span title="Category"><img alt="" src="//upload.wikimedia.org/wikipedia/en/thumb/9/96/Symbol_category_class.svg/16px-Symbol_category_class.svg.png" decoding="async" width="16" height="16" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/9/96/Symbol_category_class.svg/23px-Symbol_category_class.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/9/96/Symbol_category_class.svg/31px-Symbol_category_class.svg.png 2x" data-file-width="180" data-file-height="185" /></span></span> <a href="/wiki/Category:Google_DeepMind" title="Category:Google DeepMind">Category</a></li> <li><span class="noviewer" typeof="mw:File"><span title="Commons page"><img alt="" src="//upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/12px-Commons-logo.svg.png" decoding="async" width="12" height="16" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/18px-Commons-logo.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/24px-Commons-logo.svg.png 2x" data-file-width="1024" data-file-height="1376" /></span></span> <a href="https://commons.wikimedia.org/wiki/Category:DeepMind" class="extiw" title="commons:Category:DeepMind">Commons</a></li></ul> </div></td></tr></tbody></table></div> <div class="navbox-styles"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374" /><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236075235" /></div><div role="navigation" class="navbox" aria-labelledby="Artificial_intelligence_(AI)776" style="padding:3px"><table class="nowraplinks hlist mw-collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit"><tbody><tr><th scope="col" class="navbox-title" colspan="2"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374" /><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1239400231" /><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Artificial_intelligence_navbox" title="Template:Artificial intelligence navbox"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Artificial_intelligence_navbox" title="Template talk:Artificial intelligence navbox"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Artificial_intelligence_navbox" title="Special:EditPage/Template:Artificial intelligence navbox"><abbr title="Edit this template">e</abbr></a></li></ul></div><div id="Artificial_intelligence_(AI)776" style="font-size:114%;margin:0 4em"><a href="/wiki/Artificial_intelligence" title="Artificial intelligence">Artificial intelligence (AI)</a></div></th></tr><tr><td class="navbox-abovebelow" colspan="2"><div><a href="/wiki/History_of_artificial_intelligence" title="History of artificial intelligence">History</a> (<a href="/wiki/Timeline_of_artificial_intelligence" title="Timeline of artificial intelligence">timeline</a>)</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Concepts</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Parameter" title="Parameter">Parameter</a> <ul><li><a href="/wiki/Hyperparameter_(machine_learning)" title="Hyperparameter (machine learning)">Hyperparameter</a></li></ul></li> <li><a href="/wiki/Loss_functions_for_classification" title="Loss functions for classification">Loss functions</a></li> <li><a href="/wiki/Regression_analysis" title="Regression analysis">Regression</a> <ul><li><a href="/wiki/Bias%E2%80%93variance_tradeoff" title="Bias–variance tradeoff">Bias–variance tradeoff</a></li> <li><a href="/wiki/Double_descent" title="Double descent">Double descent</a></li> <li><a href="/wiki/Overfitting" title="Overfitting">Overfitting</a></li></ul></li> <li><a href="/wiki/Cluster_analysis" title="Cluster analysis">Clustering</a></li> <li><a href="/wiki/Gradient_descent" title="Gradient descent">Gradient descent</a> <ul><li><a href="/wiki/Stochastic_gradient_descent" title="Stochastic gradient descent">SGD</a></li> <li><a href="/wiki/Quasi-Newton_method" title="Quasi-Newton method">Quasi-Newton method</a></li> <li><a href="/wiki/Conjugate_gradient_method" title="Conjugate gradient method">Conjugate gradient method</a></li></ul></li> <li><a href="/wiki/Backpropagation" title="Backpropagation">Backpropagation</a></li> <li><a href="/wiki/Attention_(machine_learning)" title="Attention (machine learning)">Attention</a></li> <li><a href="/wiki/Convolution" title="Convolution">Convolution</a></li> <li><a href="/wiki/Normalization_(machine_learning)" title="Normalization (machine learning)">Normalization</a> <ul><li><a href="/wiki/Batch_normalization" title="Batch normalization">Batchnorm</a></li></ul></li> <li><a href="/wiki/Activation_function" title="Activation function">Activation</a> <ul><li><a href="/wiki/Softmax_function" title="Softmax function">Softmax</a></li> <li><a href="/wiki/Sigmoid_function" title="Sigmoid function">Sigmoid</a></li> <li><a href="/wiki/Rectifier_(neural_networks)" title="Rectifier (neural networks)">Rectifier</a></li></ul></li> <li><a href="/wiki/Gating_mechanism" title="Gating mechanism">Gating</a></li> <li><a href="/wiki/Weight_initialization" title="Weight initialization">Weight initialization</a></li> <li><a href="/wiki/Regularization_(mathematics)" title="Regularization (mathematics)">Regularization</a></li> <li><a href="/wiki/Training,_validation,_and_test_data_sets" title="Training, validation, and test data sets">Datasets</a> <ul><li><a href="/wiki/Data_augmentation" title="Data augmentation">Augmentation</a></li></ul></li> <li><a href="/wiki/Prompt_engineering" title="Prompt engineering">Prompt engineering</a></li> <li><a href="/wiki/Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a> <ul><li><a href="/wiki/Q-learning" title="Q-learning">Q-learning</a></li> <li><a href="/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action" title="State–action–reward–state–action">SARSA</a></li> <li><a href="/wiki/Imitation_learning" title="Imitation learning">Imitation</a></li> <li><a href="/wiki/Policy_gradient_method" title="Policy gradient method">Policy gradient</a></li></ul></li> <li><a href="/wiki/Diffusion_process" title="Diffusion process">Diffusion</a></li> <li><a href="/wiki/Latent_diffusion_model" title="Latent diffusion model">Latent diffusion model</a></li> <li><a href="/wiki/Autoregressive_model" title="Autoregressive model">Autoregression</a></li> <li><a href="/wiki/Adversarial_machine_learning" title="Adversarial machine learning">Adversary</a></li> <li><a href="/wiki/Retrieval-augmented_generation" title="Retrieval-augmented generation">RAG</a></li> <li><a href="/wiki/Uncanny_valley" title="Uncanny valley">Uncanny valley</a></li> <li><a href="/wiki/Reinforcement_learning_from_human_feedback" title="Reinforcement learning from human feedback">RLHF</a></li> <li><a href="/wiki/Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li> <li><a href="/wiki/Recursive_self-improvement" title="Recursive self-improvement">Recursive self-improvement</a></li> <li><a href="/wiki/Word_embedding" title="Word embedding">Word embedding</a></li> <li><a href="/wiki/Hallucination_(artificial_intelligence)" title="Hallucination (artificial intelligence)">Hallucination</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Applications</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a> <ul><li><a href="/wiki/Prompt_engineering#In-context_learning" title="Prompt engineering">In-context learning</a></li></ul></li> <li><a href="/wiki/Neural_network_(machine_learning)" title="Neural network (machine learning)">Artificial neural network</a> <ul><li><a href="/wiki/Deep_learning" title="Deep learning">Deep learning</a></li></ul></li> <li><a href="/wiki/Language_model" title="Language model">Language model</a> <ul><li><a href="/wiki/Large_language_model" title="Large language model">Large language model</a></li> <li><a href="/wiki/Neural_machine_translation" title="Neural machine translation">NMT</a></li></ul></li> <li><a href="/wiki/Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Implementations</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Audio–visual</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlexNet" title="AlexNet">AlexNet</a></li> <li><a href="/wiki/WaveNet" title="WaveNet">WaveNet</a></li> <li><a href="/wiki/Human_image_synthesis" title="Human image synthesis">Human image synthesis</a></li> <li><a href="/wiki/Handwriting_recognition" title="Handwriting recognition">HWR</a></li> <li><a href="/wiki/Optical_character_recognition" title="Optical character recognition">OCR</a></li> <li><a href="/wiki/Deep_learning_speech_synthesis" title="Deep learning speech synthesis">Speech synthesis</a> <ul><li><a href="/wiki/15.ai" title="15.ai">15.ai</a></li> <li><a href="/wiki/ElevenLabs" title="ElevenLabs">ElevenLabs</a></li></ul></li> <li><a href="/wiki/Speech_recognition" title="Speech recognition">Speech recognition</a> <ul><li><a href="/wiki/Whisper_(speech_recognition_system)" title="Whisper (speech recognition system)">Whisper</a></li></ul></li> <li><a href="/wiki/Facial_recognition_system" title="Facial recognition system">Facial recognition</a></li> <li><a class="mw-selflink selflink">AlphaFold</a></li> <li><a href="/wiki/Text-to-image_model" title="Text-to-image model">Text-to-image models</a> <ul><li><a href="/wiki/Aurora_(text-to-image_model)" class="mw-redirect" title="Aurora (text-to-image model)">Aurora</a></li> <li><a href="/wiki/DALL-E" title="DALL-E">DALL-E</a></li> <li><a href="/wiki/Adobe_Firefly" title="Adobe Firefly">Firefly</a></li> <li><a href="/wiki/Flux_(text-to-image_model)" title="Flux (text-to-image model)">Flux</a></li> <li><a href="/wiki/Ideogram_(text-to-image_model)" title="Ideogram (text-to-image model)">Ideogram</a></li> <li><a href="/wiki/Google_Brain#Text-to-image_model" title="Google Brain">Imagen</a></li> <li><a href="/wiki/Midjourney" title="Midjourney">Midjourney</a></li> <li><a href="/wiki/Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a></li></ul></li> <li><a href="/wiki/Text-to-video_model" title="Text-to-video model">Text-to-video models</a> <ul><li><a href="/wiki/Dream_Machine_(text-to-video_model)" title="Dream Machine (text-to-video model)">Dream Machine</a></li> <li><a href="/wiki/Runway_(company)#Gen-3_Alpha" title="Runway (company)">Gen-3 Alpha</a></li> <li><a href="/wiki/MiniMax_(company)#Hailuo_AI" title="MiniMax (company)">Hailuo AI</a></li> <li><a href="/wiki/Kling_(text-to-video_model)" class="mw-redirect" title="Kling (text-to-video model)">Kling</a></li> <li><a href="/wiki/Sora_(text-to-video_model)" title="Sora (text-to-video model)">Sora</a></li> <li><a href="/wiki/Google_DeepMind#Video_model" title="Google DeepMind">Veo</a></li></ul></li> <li><a href="/wiki/Music_and_artificial_intelligence" title="Music and artificial intelligence">Music generation</a> <ul><li><a href="/wiki/Suno_AI" title="Suno AI">Suno AI</a></li> <li><a href="/wiki/Udio" title="Udio">Udio</a></li></ul></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Text</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Word2vec" title="Word2vec">Word2vec</a></li> <li><a href="/wiki/Seq2seq" title="Seq2seq">Seq2seq</a></li> <li><a href="/wiki/GloVe" title="GloVe">GloVe</a></li> <li><a href="/wiki/BERT_(language_model)" title="BERT (language model)">BERT</a></li> <li><a href="/wiki/T5_(language_model)" title="T5 (language model)">T5</a></li> <li><a href="/wiki/Llama_(language_model)" title="Llama (language model)">Llama</a></li> <li><a href="/wiki/Chinchilla_(language_model)" title="Chinchilla (language model)">Chinchilla AI</a></li> <li><a href="/wiki/PaLM" title="PaLM">PaLM</a></li> <li><a href="/wiki/Generative_pre-trained_transformer" title="Generative pre-trained transformer">GPT</a> <ul><li><a href="/wiki/GPT-1" title="GPT-1">1</a></li> <li><a href="/wiki/GPT-2" title="GPT-2">2</a></li> <li><a href="/wiki/GPT-3" title="GPT-3">3</a></li> <li><a href="/wiki/GPT-J" title="GPT-J">J</a></li> <li><a href="/wiki/ChatGPT" title="ChatGPT">ChatGPT</a></li> <li><a href="/wiki/GPT-4" title="GPT-4">4</a></li> <li><a href="/wiki/GPT-4o" title="GPT-4o">4o</a></li> <li><a href="/wiki/GPT-4.5" title="GPT-4.5">4.5</a></li> <li><a href="/wiki/OpenAI_o1" title="OpenAI o1">o1</a></li> <li><a href="/wiki/OpenAI_o3" title="OpenAI o3">o3</a></li></ul></li> <li><a href="/wiki/Claude_(language_model)" title="Claude (language model)">Claude</a></li> <li><a href="/wiki/Gemini_(language_model)" title="Gemini (language model)">Gemini</a> <ul><li><a href="/wiki/Gemini_(chatbot)" title="Gemini (chatbot)">chatbot</a></li></ul></li> <li><a href="/wiki/Grok_(chatbot)" title="Grok (chatbot)">Grok</a></li> <li><a href="/wiki/LaMDA" title="LaMDA">LaMDA</a></li> <li><a href="/wiki/BLOOM_(language_model)" title="BLOOM (language model)">BLOOM</a></li> <li><a href="/wiki/Project_Debater" title="Project Debater">Project Debater</a></li> <li><a href="/wiki/IBM_Watson" title="IBM Watson">IBM Watson</a></li> <li><a href="/wiki/IBM_Watsonx" title="IBM Watsonx">IBM Watsonx</a></li> <li><a href="/wiki/IBM_Granite" title="IBM Granite">Granite</a></li> <li><a href="/wiki/Huawei_PanGu" title="Huawei PanGu">PanGu-Σ</a></li> <li><a href="/wiki/DeepSeek" title="DeepSeek">DeepSeek</a></li> <li><a href="/wiki/Qwen" title="Qwen">Qwen</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Decisional</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a></li> <li><a href="/wiki/AlphaZero" title="AlphaZero">AlphaZero</a></li> <li><a href="/wiki/OpenAI_Five" title="OpenAI Five">OpenAI Five</a></li> <li><a href="/wiki/Self-driving_car" title="Self-driving car">Self-driving car</a></li> <li><a href="/wiki/MuZero" title="MuZero">MuZero</a></li> <li><a href="/wiki/Action_selection" title="Action selection">Action selection</a> <ul><li><a href="/wiki/AutoGPT" title="AutoGPT">AutoGPT</a></li></ul></li> <li><a href="/wiki/Robot_control" title="Robot control">Robot control</a></li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">People</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Alan_Turing" title="Alan Turing">Alan Turing</a></li> <li><a href="/wiki/Warren_Sturgis_McCulloch" title="Warren Sturgis McCulloch">Warren Sturgis McCulloch</a></li> <li><a href="/wiki/Walter_Pitts" title="Walter Pitts">Walter Pitts</a></li> <li><a href="/wiki/John_von_Neumann" title="John von Neumann">John von Neumann</a></li> <li><a href="/wiki/Claude_Shannon" title="Claude Shannon">Claude Shannon</a></li> <li><a href="/wiki/Marvin_Minsky" title="Marvin Minsky">Marvin Minsky</a></li> <li><a href="/wiki/John_McCarthy_(computer_scientist)" title="John McCarthy (computer scientist)">John McCarthy</a></li> <li><a href="/wiki/Nathaniel_Rochester_(computer_scientist)" title="Nathaniel Rochester (computer scientist)">Nathaniel Rochester</a></li> <li><a href="/wiki/Allen_Newell" title="Allen Newell">Allen Newell</a></li> <li><a href="/wiki/Cliff_Shaw" title="Cliff Shaw">Cliff Shaw</a></li> <li><a href="/wiki/Herbert_A._Simon" title="Herbert A. Simon">Herbert A. Simon</a></li> <li><a href="/wiki/Oliver_Selfridge" title="Oliver Selfridge">Oliver Selfridge</a></li> <li><a href="/wiki/Frank_Rosenblatt" title="Frank Rosenblatt">Frank Rosenblatt</a></li> <li><a href="/wiki/Bernard_Widrow" title="Bernard Widrow">Bernard Widrow</a></li> <li><a href="/wiki/Joseph_Weizenbaum" title="Joseph Weizenbaum">Joseph Weizenbaum</a></li> <li><a href="/wiki/Seymour_Papert" title="Seymour Papert">Seymour Papert</a></li> <li><a href="/wiki/Seppo_Linnainmaa" title="Seppo Linnainmaa">Seppo Linnainmaa</a></li> <li><a href="/wiki/Paul_Werbos" title="Paul Werbos">Paul Werbos</a></li> <li><a href="/wiki/J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a></li> <li><a href="/wiki/Yann_LeCun" title="Yann LeCun">Yann LeCun</a></li> <li><a href="/wiki/Geoffrey_Hinton" title="Geoffrey Hinton">Geoffrey Hinton</a></li> <li><a href="/wiki/John_Hopfield" title="John Hopfield">John Hopfield</a></li> <li><a href="/wiki/Yoshua_Bengio" title="Yoshua Bengio">Yoshua Bengio</a></li> <li><a href="/wiki/Lotfi_A._Zadeh" title="Lotfi A. Zadeh">Lotfi A. Zadeh</a></li> <li><a href="/wiki/Stephen_Grossberg" title="Stephen Grossberg">Stephen Grossberg</a></li> <li><a href="/wiki/Alex_Graves_(computer_scientist)" title="Alex Graves (computer scientist)">Alex Graves</a></li> <li><a href="/wiki/Andrew_Ng" title="Andrew Ng">Andrew Ng</a></li> <li><a href="/wiki/Fei-Fei_Li" title="Fei-Fei Li">Fei-Fei Li</a></li> <li><a href="/wiki/Alex_Krizhevsky" title="Alex Krizhevsky">Alex Krizhevsky</a></li> <li><a href="/wiki/Ilya_Sutskever" title="Ilya Sutskever">Ilya Sutskever</a></li> <li><a href="/wiki/Demis_Hassabis" title="Demis Hassabis">Demis Hassabis</a></li> <li><a href="/wiki/David_Silver_(computer_scientist)" title="David Silver (computer scientist)">David Silver</a></li> <li><a href="/wiki/Ian_Goodfellow" title="Ian Goodfellow">Ian Goodfellow</a></li> <li><a href="/wiki/Andrej_Karpathy" title="Andrej Karpathy">Andrej Karpathy</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Architectures</th><td 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state network</a></li> <li><a href="/wiki/Multilayer_perceptron" title="Multilayer perceptron">Multilayer perceptron (MLP)</a></li> <li><a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network (CNN)</a></li> <li><a href="/wiki/Residual_neural_network" title="Residual neural network">Residual neural network (RNN)</a></li> <li><a href="/wiki/Highway_network" title="Highway network">Highway network</a></li> <li><a href="/wiki/Mamba_(deep_learning_architecture)" title="Mamba (deep learning architecture)">Mamba</a></li> <li><a href="/wiki/Autoencoder" title="Autoencoder">Autoencoder</a></li> <li><a href="/wiki/Variational_autoencoder" title="Variational autoencoder">Variational autoencoder (VAE)</a></li> <li><a href="/wiki/Generative_adversarial_network" title="Generative adversarial network">Generative adversarial network (GAN)</a></li> <li><a href="/wiki/Graph_neural_network" title="Graph neural network">Graph neural network 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